==============================
Datasets Available in Matminer
==============================

Below you will find descriptions and reference data on each available dataset, ordered by load_dataset() keyword argument


------------
boltztrap_mp
------------
Effective mass and thermoelectric properties of 8924 compounds in The  Materials Project database that are calculated by the BoltzTraP software package run on the GGA-PBE or GGA+U density functional theory calculation results. The properties are reported at the temperature of 300 Kelvin and the carrier concentration of 1e18 1/cm3.

**Number of entries:** 8924

========= ======================================================================================================================
Column    Description
========= ======================================================================================================================
formula   Chemical formula of the entry
m_n       n-type/conduction band effective mass. Units: m_e where m_e is the mass of an electron; i.e. m_n is a unitless ratio
m_p       p-type/valence band effective mass.
mpid      Materials Project identifier
pf_n      n-type thermoelectric power factor in uW/cm2.K where uW is microwatts and a constant relaxation time of 1e-14 assumed.
pf_p      p-type power factor in uW/cm2.K
s_n       n-type Seebeck coefficient in micro Volts per Kelvin
s_p       p-type Seebeck coefficient in micro Volts per Kelvin
structure pymatgen Structure object describing the crystal structure of the material
========= ======================================================================================================================



**Reference**

Ricci, F. et al. An ab initio electronic transport database for inorganic materials. Sci. Data 4:170085 doi: 10.1038/sdata.2017.85 (2017).
Ricci F, Chen W, Aydemir U, Snyder J, Rignanese G, Jain A, Hautier G (2017) Data from: An ab initio electronic transport database for inorganic materials. Dryad Digital Repository. https://doi.org/10.5061/dryad.gn001



**Bibtex Formatted Citations**

@Article{Ricci2017,
author={Ricci, Francesco
and Chen, Wei
and Aydemir, Umut
and Snyder, G. Jeffrey
and Rignanese, Gian-Marco
and Jain, Anubhav
and Hautier, Geoffroy},
title={An ab initio electronic transport database for inorganic materials},
journal={Scientific Data},
year={2017},
month={Jul},
day={04},
publisher={The Author(s)},
volume={4},
pages={170085},
note={Data Descriptor},
url={http://dx.doi.org/10.1038/sdata.2017.85}
}

@misc{dryad_gn001,
title = {Data from: An ab initio electronic transport database for inorganic materials},
author = {Ricci, F and Chen, W and Aydemir, U and Snyder, J and Rignanese, G and Jain, A and Hautier, G},
year = {2017},
journal = {Scientific Data},
URL = {https://doi.org/10.5061/dryad.gn001},
doi = {doi:10.5061/dryad.gn001},
publisher = {Dryad Digital Repository}
}




-------------------------
brgoch_superhard_training
-------------------------
2574 materials used for training regressors that predict shear and bulk modulus.

**Number of entries:** 2574

============= ============================================================================================================================================================
Column        Description
============= ============================================================================================================================================================
brgoch_feats  features used in brgoch study compressed to a dictionary
bulk_modulus  VRH bulk modulus
composition   pymatgen composition object
formula       Chemical formula as a string
material_id   materials project id
structure     pymatgen structure object
shear_modulus VRH shear modulus
suspect_value True if bulk or shear value did not closely match (within 5%/1GPa of MP) materials project value at time of cross reference or if no material could be found
============= ============================================================================================================================================================



**Reference**

Machine Learning Directed Search for Ultraincompressible, Superhard Materials
Aria Mansouri Tehrani, Anton O. Oliynyk, Marcus Parry, Zeshan Rizvi, Samantha Couper, Feng Lin, Lowell Miyagi, Taylor D. Sparks, and Jakoah Brgoch
Journal of the American Chemical Society 2018 140 (31), 9844-9853
DOI: 10.1021/jacs.8b02717



**Bibtex Formatted Citations**

@article{doi:10.1021/jacs.8b02717,
author = {Mansouri Tehrani, Aria and Oliynyk, Anton O. and Parry, Marcus and Rizvi, Zeshan and Couper, Samantha and Lin, Feng and Miyagi, Lowell and Sparks, Taylor D. and Brgoch, Jakoah},
title = {Machine Learning Directed Search for Ultraincompressible, Superhard Materials},
journal = {Journal of the American Chemical Society},
volume = {140},
number = {31},
pages = {9844-9853},
year = {2018},
doi = {10.1021/jacs.8b02717},
note ={PMID: 30010335},
URL = {
https://doi.org/10.1021/jacs.8b02717
},
eprint = {
https://doi.org/10.1021/jacs.8b02717
}
}




--------------------
castelli_perovskites
--------------------
18,928 perovskites generated with ABX combinatorics, calculating gllbsc band gap and pbe structure, and also reporting absolute band edge positions and heat of formation.

**Number of entries:** 18928

============= =====================================================================
Column        Description
============= =====================================================================
cbm           similar to vbm but for conduction band
e_form        heat of formation in eV
fermi level   the thermodynamic work required to add one electron to the body in eV
fermi width   fermi bandwidth
formula       Chemical formula of the material
gap gllbsc    electronic band gap in eV calculated via gllbsc functional
gap is direct boolean indicator for direct gap
mu_b          magnetic moment in terms of Bohr magneton
structure     crystal structure represented by pymatgen Structure object
vbm           absolute value of valence band edge calculated via gllbsc
============= =====================================================================



**Reference**

Ivano E. Castelli, David D. Landis, Kristian S. Thygesen, Søren Dahl, Ib Chorkendorff, Thomas F. Jaramillo and Karsten W. Jacobsen (2012) New cubic perovskites for one- and two-photon water splitting using the computational materials repository. Energy Environ. Sci., 2012,5, 9034-9043 https://doi.org/10.1039/C2EE22341D



**Bibtex Formatted Citations**

@Article{C2EE22341D,
author ="Castelli, Ivano E. and Landis, David D. and Thygesen, Kristian S. and Dahl, Søren and Chorkendorff, Ib and Jaramillo, Thomas F. and Jacobsen, Karsten W.",
title  ="New cubic perovskites for one- and two-photon water splitting using the computational materials repository",
journal  ="Energy Environ. Sci.",
year  ="2012",
volume  ="5",
issue  ="10",
pages  ="9034-9043",
publisher  ="The Royal Society of Chemistry",
doi  ="10.1039/C2EE22341D",
url  ="http://dx.doi.org/10.1039/C2EE22341D",
abstract  ="A new efficient photoelectrochemical cell (PEC) is one of the possible solutions to the energy and climate problems of our time. Such a device requires development of new semiconducting materials with tailored properties with respect to stability and light absorption. Here we perform computational screening of around 19 000 oxides{,} oxynitrides{,} oxysulfides{,} oxyfluorides{,} and oxyfluoronitrides in the cubic perovskite structure with PEC applications in mind. We address three main applications: light absorbers for one- and two-photon water splitting and high-stability transparent shields to protect against corrosion. We end up with 20{,} 12{,} and 15 different combinations of oxides{,} oxynitrides and oxyfluorides{,} respectively{,} inviting further experimental investigation."}




----------------------------
citrine_thermal_conductivity
----------------------------
Thermal conductivity of 872 compounds measured experimentally and retrieved from Citrine database from various references. The reported values are measured at various temperatures of which 295 are at room temperature.

**Number of entries:** 872

================= =====================================================================
Column            Description
================= =====================================================================
formula           Chemical formula of the dataset entry
k-units           units of thermal conductivity
k_condition       Temperature description of testing conditions
k_condition_units units of testing condition temperature representation
k_expt            the experimentally measured thermal conductivity in SI units of W/m.K
================= =====================================================================



**Reference**

https://www.citrination.com



**Bibtex Formatted Citations**

@misc{Citrine Informatics,
title = {Citrination},
howpublished = {\url{https://www.citrination.com/}},
}




-------------------
dielectric_constant
-------------------
1,056 structures with dielectric properties, calculated with DFPT-PBE.

**Number of entries:** 1056

================= ==================================================================================================================================
Column            Description
================= ==================================================================================================================================
band_gap          Measure of the conductivity of a material
cif               optional: Description string for structure
e_electronic      electronic contribution to dielectric tensor
e_total           Total dielectric tensor incorporating both electronic and ionic contributions
formula           Chemical formula of the material
material_id       Materials Project ID of the material
meta              optional, metadata descriptor of the datapoint
n                 Refractive Index
nsites            The \# of atoms in the unit cell of the calculation.
poly_electronic   the average of the eigenvalues of the electronic contribution to the dielectric tensor
poly_total        the average of the eigenvalues of the total (electronic and ionic) contributions to the dielectric tensor
poscar            optional: Poscar metadata
pot_ferroelectric Whether the material is potentially ferroelectric
space_group       Integer specifying the crystallographic structure of the material
structure         pandas Series defining the structure of the material
volume            Volume of the unit cell in cubic angstroms, For supercell calculations, this quantity refers to the volume of the full supercell. 
================= ==================================================================================================================================



**Reference**

Petousis, I., Mrdjenovich, D., Ballouz, E., Liu, M., Winston, D.,
Chen, W., Graf, T., Schladt, T. D., Persson, K. A. & Prinz, F. B.
High-throughput screening of inorganic compounds for the discovery
of novel dielectric and optical materials. Sci. Data 4, 160134 (2017).



**Bibtex Formatted Citations**

@Article{Petousis2017,
author={Petousis, Ioannis and Mrdjenovich, David and Ballouz, Eric
and Liu, Miao and Winston, Donald and Chen, Wei and Graf, Tanja
and Schladt, Thomas D. and Persson, Kristin A. and Prinz, Fritz B.},
title={High-throughput screening of inorganic compounds for the
discovery of novel dielectric and optical materials},
journal={Scientific Data},
year={2017},
month={Jan},
day={31},
publisher={The Author(s)},
volume={4},
pages={160134},
note={Data Descriptor},
url={http://dx.doi.org/10.1038/sdata.2016.134}
}




----------------------
double_perovskites_gap
----------------------
Band gap of 1306 double perovskites (a_1-b_1-a_2-b_2-O6) calculated using ﻿Gritsenko, van Leeuwen, van Lenthe and Baerends potential (gllbsc) in GPAW.

**Number of entries:** 1306

========== =================================================
Column     Description
========== =================================================
a_1        Species occupying the a1 perovskite site
a_2        Species occupying the a2 site
b_1        Species occupying the b1 site
b_2        Species occupying the b2 site
formula    Chemical formula of the entry
gap gllbsc electronic band gap (in eV) calculated via gllbsc
========== =================================================



**Reference**

Dataset discussed in:
Pilania, G. et al. Machine learning bandgaps of double perovskites. Sci. Rep. 6, 19375; doi: 10.1038/srep19375 (2016).
Dataset sourced from:
https://cmr.fysik.dtu.dk/



**Bibtex Formatted Citations**

@Article{Pilania2016,
author={Pilania, G.
and Mannodi-Kanakkithodi, A.
and Uberuaga, B. P.
and Ramprasad, R.
and Gubernatis, J. E.
and Lookman, T.},
title={Machine learning bandgaps of double perovskites},
journal={Scientific Reports},
year={2016},
month={Jan},
day={19},
publisher={The Author(s)},
volume={6},
pages={19375},
note={Article},
url={http://dx.doi.org/10.1038/srep19375}
}

@misc{Computational Materials Repository,
title = {Computational Materials Repository},
howpublished = {\url{https://cmr.fysik.dtu.dk/}},
}




---------------------------
double_perovskites_gap_lumo
---------------------------
Supplementary lumo data of 55 atoms for the double_perovskites_gap dataset.

**Number of entries:** 55

====== =======================================================
Column Description
====== =======================================================
atom   Name of the atom whos lumo is listed
lumo   Lowest unoccupied molecular obital energy level (in eV)
====== =======================================================



**Reference**

Dataset discussed in:
Pilania, G. et al. Machine learning bandgaps of double perovskites. Sci. Rep. 6, 19375; doi: 10.1038/srep19375 (2016).
Dataset sourced from:
https://cmr.fysik.dtu.dk/



**Bibtex Formatted Citations**

@Article{Pilania2016,
author={Pilania, G.
and Mannodi-Kanakkithodi, A.
and Uberuaga, B. P.
and Ramprasad, R.
and Gubernatis, J. E.
and Lookman, T.},
title={Machine learning bandgaps of double perovskites},
journal={Scientific Reports},
year={2016},
month={Jan},
day={19},
publisher={The Author(s)},
volume={6},
pages={19375},
note={Article},
url={http://dx.doi.org/10.1038/srep19375}
}

@misc{Computational Materials Repository,
title = {Computational Materials Repository},
howpublished = {\url{https://cmr.fysik.dtu.dk/}},
}




-------------------
elastic_tensor_2015
-------------------
1,181 structures with elastic properties calculated with DFT-PBE.

**Number of entries:** 1181

======================= ==================================================================================================================================
Column                  Description
======================= ==================================================================================================================================
G_Reuss                 Lower bound on shear modulus for polycrystalline material
G_VRH                   Average of G_Reuss and G_Voigt
G_Voigt                 Upper bound on shear modulus for polycrystalline material
K_Reuss                 Lower bound on bulk modulus for polycrystalline material
K_VRH                   Average of K_Reuss and K_Voigt
K_Voigt                 Upper bound on bulk modulus for polycrystalline material
cif                     optional: Description string for structure
compliance_tensor       Tensor describing elastic behavior
elastic_anisotropy      measure of directional dependence of the materials elasticity, metric is always >= 0
elastic_tensor          Tensor describing elastic behavior corresponding to IEEE orientation, symmetrized to crystal structure 
elastic_tensor_original Tensor describing elastic behavior, unsymmetrized, corresponding to POSCAR conventional standard cell orientation
formula                 Chemical formula of the material
kpoint_density          optional: Sampling parameter from calculation
material_id             Materials Project ID of the material
nsites                  The \# of atoms in the unit cell of the calculation.
poisson_ratio           Describes lateral response to loading
poscar                  optional: Poscar metadata
space_group             Integer specifying the crystallographic structure of the material
structure               pandas Series defining the structure of the material
volume                  Volume of the unit cell in cubic angstroms, For supercell calculations, this quantity refers to the volume of the full supercell. 
======================= ==================================================================================================================================



**Reference**

Jong, M. De, Chen, W., Angsten, T., Jain, A., Notestine, R., Gamst,
A., Sluiter, M., Ande, C. K., Zwaag, S. Van Der, Plata, J. J., Toher,
C., Curtarolo, S., Ceder, G., Persson, K. and Asta, M., "Charting
the complete elastic properties of inorganic crystalline compounds",
Scientific Data volume 2, Article number: 150009 (2015)



**Bibtex Formatted Citations**

@Article{deJong2015,
author={de Jong, Maarten and Chen, Wei and Angsten, Thomas
and Jain, Anubhav and Notestine, Randy and Gamst, Anthony
and Sluiter, Marcel and Krishna Ande, Chaitanya
and van der Zwaag, Sybrand and Plata, Jose J. and Toher, Cormac
and Curtarolo, Stefano and Ceder, Gerbrand and Persson, Kristin A.
and Asta, Mark},
title={Charting the complete elastic properties
of inorganic crystalline compounds},
journal={Scientific Data},
year={2015},
month={Mar},
day={17},
publisher={The Author(s)},
volume={2},
pages={150009},
note={Data Descriptor},
url={http://dx.doi.org/10.1038/sdata.2015.9}
}




-----------------------
expt_formation_enthalpy
-----------------------
Experimental formation enthalpies for inorganic compounds, collected from years of calorimetric experiments. There are 1,276 entries in this dataset, mostly binary compounds. Matching mpids or oqmdids as well as the DFT-computed formation energies are also added (if any).

**Number of entries:** 1276

============== ======================================================
Column         Description
============== ======================================================
e_form expt    experimental formation enthalpy (in eV/atom)
e_form mp      formation enthalpy from Materials Project (in eV/atom)
e_form oqmd    formation enthalpy from OQMD (in eV/atom)
formula        chemical formula
mpid           materials project id
oqmdid         OQMD id
pearson symbol pearson symbol of the structure
space group    space group of the structure
============== ======================================================



**Reference**

https://www.nature.com/articles/sdata2017162



**Bibtex Formatted Citations**

@Article{Kim2017,
author={Kim, George
and Meschel, S. V.
and Nash, Philip
and Chen, Wei},
title={Experimental formation enthalpies for intermetallic phases and other inorganic compounds},
journal={Scientific Data},
year={2017},
month={Oct},
day={24},
publisher={The Author(s)},
volume={4},
pages={170162},
note={Data Descriptor},
url={https://doi.org/10.1038/sdata.2017.162}}

 @misc{kim_meschel_nash_chen_2017, title={Experimental formation enthalpies for intermetallic phases and other inorganic compounds}, url={https://figshare.com/collections/Experimental_formation_enthalpies_for_intermetallic_phases_and_other_inorganic_compounds/3822835/1}, DOI={10.6084/m9.figshare.c.3822835.v1}, abstractNote={The standard enthalpy of formation of a compound is the energy associated with the reaction to form the compound from its component elements. The standard enthalpy of formation is a fundamental thermodynamic property that determines its phase stability, which can be coupled with other thermodynamic data to calculate phase diagrams. Calorimetry provides the only direct method by which the standard enthalpy of formation is experimentally measured. However, the measurement is often a time and energy intensive process. We present a dataset of enthalpies of formation measured by high-temperature calorimetry. The phases measured in this dataset include intermetallic compounds with transition metal and rare-earth elements, metal borides, metal carbides, and metallic silicides. These measurements were collected from over 50 years of calorimetric experiments. The dataset contains 1,276 entries on experimental enthalpy of formation values and structural information. Most of the entries are for binary compounds but ternary and quaternary compounds are being added as they become available. The dataset also contains predictions of enthalpy of formation from first-principles calculations for comparison.}, publisher={figshare}, author={Kim, George and Meschel, Susan and Nash, Philip and Chen, Wei}, year={2017}, month={Oct}}




--------
expt_gap
--------
Experimental band gap of 6354 inorganic semiconductors.

**Number of entries:** 6354

======== ========================================
Column   Description
======== ========================================
formula  chemical formula
gap expt band gap (in eV) measured experimentally
======== ========================================



**Reference**

https://pubs.acs.org/doi/suppl/10.1021/acs.jpclett.8b00124



**Bibtex Formatted Citations**

@article{doi:10.1021/acs.jpclett.8b00124,
author = {Zhuo, Ya and Mansouri Tehrani, Aria and Brgoch, Jakoah},
title = {Predicting the Band Gaps of Inorganic Solids by Machine Learning},
journal = {The Journal of Physical Chemistry Letters},
volume = {9},
number = {7},
pages = {1668-1673},
year = {2018},
doi = {10.1021/acs.jpclett.8b00124},
note ={PMID: 29532658},
eprint = {
https://doi.org/10.1021/acs.jpclett.8b00124

}}




----
flla
----
3938 structures and computed formation energies from "Crystal Structure Representations for Machine Learning Models of Formation Energies."

**Number of entries:** 3938

========================= ============================================================================================================================
Column                    Description
========================= ============================================================================================================================
e_above_hull              The energy of decomposition of this material into the set of most stable materials at this chemical composition, in eV/atom.
formation_energy          Computed formation energy at 0K, 0atm using a reference state of zero for the pure elements.
formation_energy_per_atom See formation_energy
formula                   Chemical formula of the material
material_id               Materials Project ID of the material
nsites                    The \# of atoms in the unit cell of the calculation.
structure                 pandas Series defining the structure of the material
========================= ============================================================================================================================



**Reference**

1) F. Faber, A. Lindmaa, O.A. von Lilienfeld, R. Armiento,
"Crystal structure representations for machine learning models of
formation energies", Int. J. Quantum Chem. 115 (2015) 1094–1101.
doi:10.1002/qua.24917.

(raw data)
2) Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D.,
Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G. & Persson,
K. A. Commentary: The Materials Project: A materials genome approach
to accelerating materials innovation. APL Mater. 1, 11002 (2013).



**Bibtex Formatted Citations**

@article{doi:10.1002/qua.24917,
author = {Faber, Felix and Lindmaa, Alexander and von Lilienfeld, O. Anatole and Armiento, Rickard},
title = {Crystal structure representations for machine learning models of formation energies},
journal = {International Journal of Quantum Chemistry},
volume = {115},
number = {16},
pages = {1094-1101},
keywords = {machine learning, formation energies, representations,
crystal structure, periodic systems},
doi = {10.1002/qua.24917},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/qua.24917},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/qua.24917},
abstract = {We introduce and evaluate a set of feature vector
representations of crystal structures for machine learning (ML)
models of formation energies of solids. ML models of atomization
energies of organic molecules have been successful using a Coulomb
matrix representation of the molecule. We consider three ways to
generalize such representations to periodic systems: (i) a matrix
where each element is related to the Ewald sum of the electrostatic
interaction between two different atoms in the unit cell repeated
over the lattice; (ii) an extended Coulomb-like matrix that takes
into account a number of neighboring unit cells; and (iii) an
ansatz that mimics the periodicity and the basic features of the
elements in the Ewald sum matrix using a sine function of the
crystal coordinates of the atoms. The representations are compared
for a Laplacian kernel with Manhattan norm, trained to reproduce
formation energies using a dataset of 3938 crystal structures
obtained from the Materials Project. For training sets consisting
of 3000 crystals, the generalization error in predicting formation
energies of new structures corresponds to (i) 0.49, (ii) 0.64, and
(iii) for the respective representations. © 2015 Wiley Periodicals,
Inc.}
}

@article{doi:10.1063/1.4812323,
author = {Jain,Anubhav  and Ong,Shyue Ping  and Hautier,Geoffroy
and Chen,Wei  and Richards,William Davidson  and Dacek,Stephen
and Cholia,Shreyas  and Gunter,Dan  and Skinner,David
and Ceder,Gerbrand  and Persson,Kristin A. },
title = {Commentary: The Materials Project: A materials genome
approach to accelerating materials innovation},
journal = {APL Materials},
volume = {1},
number = {1},
pages = {011002},
year = {2013},
doi = {10.1063/1.4812323},
URL = {https://doi.org/10.1063/1.4812323},
eprint = {https://doi.org/10.1063/1.4812323}
}




------------
glass_binary
------------
Metallic glass formation data for binary alloys, collected from various experimental techniques such as melt-spinning or mechanical alloying. This dataset covers all compositions with an interval of 5 at. % in 59 binary systems, containing a total of 5959 alloys in the dataset. The target property of this dataset is the glass forming ability (GFA), i.e. whether the composition can form monolithic glass or not, which is either 1 for glass forming or 0 for non-full glass forming.

**Number of entries:** 5959

======= =================================================================================================================================================================================
Column  Description
======= =================================================================================================================================================================================
formula chemical formula
gfa     glass forming ability, correlated with the phase column, designating whether the composition can form monolithic glass or not, 1: glass forming ("AM"), 0: non-full-forming("CR")
======= =================================================================================================================================================================================



**Reference**

https://pubs.acs.org/doi/10.1021/acs.jpclett.7b01046



**Bibtex Formatted Citations**

@article{doi:10.1021/acs.jpclett.7b01046,
author = {Sun, Y. T. and Bai, H. Y. and Li, M. Z. and Wang, W. H.},
title = {Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability},
journal = {The Journal of Physical Chemistry Letters},
volume = {8},
number = {14},
pages = {3434-3439},
year = {2017},
doi = {10.1021/acs.jpclett.7b01046},
note ={PMID: 28697303},
eprint = {
https://doi.org/10.1021/acs.jpclett.7b01046

}}




---------------
glass_binary_v2
---------------
Identical to glass_binary dataset, but with duplicate entries merged. If there was a disagreement in gfa when merging the class was defaulted to 1.

**Number of entries:** 5483

======= =================================================================================================================================================================================
Column  Description
======= =================================================================================================================================================================================
formula chemical formula
gfa     glass forming ability, correlated with the phase column, designating whether the composition can form monolithic glass or not, 1: glass forming ("AM"), 0: non-full-forming("CR")
======= =================================================================================================================================================================================



**Reference**

https://pubs.acs.org/doi/10.1021/acs.jpclett.7b01046



**Bibtex Formatted Citations**

@article{doi:10.1021/acs.jpclett.7b01046,
author = {Sun, Y. T. and Bai, H. Y. and Li, M. Z. and Wang, W. H.},
title = {Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability},
journal = {The Journal of Physical Chemistry Letters},
volume = {8},
number = {14},
pages = {3434-3439},
year = {2017},
doi = {10.1021/acs.jpclett.7b01046},
note ={PMID: 28697303},
eprint = {
https://doi.org/10.1021/acs.jpclett.7b01046

}}




------------------
glass_ternary_hipt
------------------
Metallic glass formation dataset for ternary alloys, collected from the high-throughput sputtering experiments measuring whether it is possible to form a glass using sputtering. The hipt experimental data are of the Co-Fe-Zr, Co-Ti-Zr, Co-V-Zr and Fe-Ti-Nb ternary systems.

**Number of entries:** 5170

========== ==================================================================================================================
Column     Description
========== ==================================================================================================================
formula    Chemical formula of the entry
gfa        Glass forming ability: 1 means glass forming and coresponds to AM, 0 means non glass forming and corresponds to CR
phase      AM: amorphous phase or CR: crystalline phase
processing How the point was processed, always sputtering for this dataset
system     System of dataset experiment, one of: CoFeZr, CoTiZr, CoVZr, or FeTiNb
========== ==================================================================================================================



**Reference**

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments
By Fang Ren, Logan Ward, Travis Williams, Kevin J. Laws, Christopher Wolverton, Jason Hattrick-Simpers, Apurva Mehta
Science Advances 13 Apr 2018 : eaaq1566



**Bibtex Formatted Citations**

@article {Reneaaq1566,
author = {Ren, Fang and Ward, Logan and Williams, Travis and Laws, Kevin J. and Wolverton, Christopher and Hattrick-Simpers, Jason and Mehta, Apurva},
title = {Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments},
volume = {4},
number = {4},
year = {2018},
doi = {10.1126/sciadv.aaq1566},
publisher = {American Association for the Advancement of Science},
abstract = {With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method{\textendash}dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, but there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method{\textendash}sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path{\textendash}dependent and that current physiochemical theories find challenging to predict.},
URL = {http://advances.sciencemag.org/content/4/4/eaaq1566},
eprint = {http://advances.sciencemag.org/content/4/4/eaaq1566.full.pdf},
journal = {Science Advances}
}




---------------------
glass_ternary_landolt
---------------------
Metallic glass formation dataset for ternary alloys, collected from the "Nonequilibrium Phase Diagrams of Ternary Amorphous Alloys,’ a volume of the Landolt– Börnstein collection. This dataset contains experimental measurements of whether it is possible to form a glass using a variety of processing techniques at thousands of compositions from hundreds of ternary systems. The processing techniques are designated in the "processing" column. There are originally 7191 experiments in this dataset, will be reduced to 6203 after deduplicated, and will be further reduced to 6118 if combining multiple data for one composition. There are originally 6780 melt-spinning experiments in this dataset, will be reduced to 5800 if deduplicated, and will be further reduced to 5736 if combining multiple experimental data for one composition.

**Number of entries:** 7191

========== ============================================================================================================================================================================
Column     Description
========== ============================================================================================================================================================================
formula    Chemical formula of the entry
gfa        Glass forming ability: 1 means glass forming and corresponds to AM, 0 means non full glass forming and corresponds to CR AC or QC
phase      "AM": amorphous phase. "CR": crystalline phase. "AC": amorphous-crystalline composite phase. "QC": quasi-crystalline phase. Phases obtained from glass producing experiments
processing processing method, meltspin or sputtering
========== ============================================================================================================================================================================



**Reference**

Y. Kawazoe, T. Masumoto, A.-P. Tsai, J.-Z. Yu, T. Aihara Jr. (1997) Y. Kawazoe, J.-Z. Yu, A.-P. Tsai, T. Masumoto (ed.) SpringerMaterials
Nonequilibrium Phase Diagrams of Ternary Amorphous Alloys · 1 Introduction Landolt-Börnstein - Group III Condensed Matter 37A (Nonequilibrium Phase Diagrams of Ternary Amorphous Alloys) https://materials.springer.com/lb/docs/sm_lbs_978-3-540-47679-5_2
10.1007/10510374_2 (Springer-Verlag Berlin Heidelberg © 1997) Accessed: 20-10-2018



**Bibtex Formatted Citations**

@Misc{LandoltBornstein1997:sm_lbs_978-3-540-47679-5_2,
author="Kawazoe, Y.
and Masumoto, T.
and Tsai, A.-P.
and Yu, J.-Z.
and Aihara Jr., T.",
editor="Kawazoe, Y.
and Yu, J.-Z.
and Tsai, A.-P.
and Masumoto, T.",
title="Nonequilibrium Phase Diagrams of Ternary Amorphous Alloys {\textperiodcentered} 1 Introduction: Datasheet from Landolt-B{\"o}rnstein - Group III Condensed Matter {\textperiodcentered} Volume 37A: ``Nonequilibrium Phase Diagrams of Ternary Amorphous Alloys'' in SpringerMaterials (https://dx.doi.org/10.1007/10510374{\_}2)",
publisher="Springer-Verlag Berlin Heidelberg",
note="Copyright 1997 Springer-Verlag Berlin Heidelberg",
note="Part of SpringerMaterials",
note="accessed 2018-10-23",
doi="10.1007/10510374_2",
url="https://materials.springer.com/lb/docs/sm_lbs_978-3-540-47679-5_2"
}

@Article{Ward2016,
author={Ward, Logan
and Agrawal, Ankit
and Choudhary, Alok
and Wolverton, Christopher},
title={A general-purpose machine learning framework for predicting properties of inorganic materials},
journal={Npj Computational Materials},
year={2016},
month={Aug},
day={26},
publisher={The Author(s)},
volume={2},
pages={16028},
note={Article},
url={http://dx.doi.org/10.1038/npjcompumats.2016.28}
}




----------------
heusler_magnetic
----------------
1153 Heusler alloys with DFT-calculated magnetic and electronic properties. The 1153 alloys include 576 full, 449 half and 128 inverse Heusler alloys. The data are extracted and cleaned (including de-duplicating) from Citrine.

**Number of entries:** 1153

=============== ====================================
Column          Description
=============== ====================================
e_form          Formation energy in eV/atom
formula         Chemical formula of the entry
heusler type    Full, Half, or Inverse Heusler
latt const      Lattice constant
mu_b            Magnetic moment
mu_b saturation Saturation magnetization in emu/cc
num_electron    Number of electrons per formula unit
pol fermi       Polarization at Fermi level in %
struct type     Structure type
tetragonality   Tetragonality, i.e. c/a
=============== ====================================



**Reference**

https://citrination.com/datasets/150561/



**Bibtex Formatted Citations**

@misc{Citrine Informatics,
title = {University of Alabama Heusler database},
howpublished = {\url{https://citrination.com/datasets/150561/}},
}




-------------
jarvis_dft_2d
-------------
Various properties of 636 2D materials computed with the OptB88vdW and TBmBJ functionals taken from the JARVIS DFT database.

**Number of entries:** 636

================= ===============================================================================
Column            Description
================= ===============================================================================
composition       A Pymatgen Composition descriptor of the composition of the material
e_form            formation energy per atom, in eV/atom
epsilon_x opt     Static dielectric function in x direction calculated with OptB88vDW functional.
epsilon_x tbmbj   Static dielectric function in x direction calculuated with TBMBJ functional.
epsilon_y opt     Static dielectric function in y direction calculated with OptB88vDW functional.
epsilon_y tbmbj   Static dielectric function in y direction calculuated with TBMBJ functional.
epsilon_z opt     Static dielectric function in z direction calculated with OptB88vDW functional.
epsilon_z tbmbj   Static dielectric function in z direction calculuated with TBMBJ functional.
exfoliation_en    Exfoliation energy (monolayer formation E) in eV
gap opt           Band gap calculated with OptB88vDW functional, in eV
gap tbmbj         Band gap calculated with TBMBJ functional, in eV
jid               JARVIS ID
mpid              Materials Project ID
structure         A description of the crystal structure of the material
structure initial Initial structure description of the crystal structure of the material
================= ===============================================================================



**Reference**

2D Dataset discussed in:
High-throughput Identification and Characterization of Two dimensional Materials using Density functional theory Kamal Choudhary, Irina Kalish, Ryan Beams & Francesca Tavazza Scientific Reports volume 7, Article number: 5179 (2017)
Original 2D Data file sourced from:
choudhary, kamal; https://orcid.org/0000-0001-9737-8074 (2018): jdft_2d-7-7-2018.json. figshare. Dataset.



**Bibtex Formatted Citations**

@Article{Choudhary2017,
author={Choudhary, Kamal
and Kalish, Irina
and Beams, Ryan
and Tavazza, Francesca},
title={High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory},
journal={Scientific Reports},
year={2017},
volume={7},
number={1},
pages={5179},
abstract={We introduce a simple criterion to identify two-dimensional (2D) materials based on the comparison between experimental lattice constants and lattice constants mainly obtained from Materials-Project (MP) density functional theory (DFT) calculation repository. Specifically, if the relative difference between the two lattice constants for a specific material is greater than or equal to 5%, we predict them to be good candidates for 2D materials. We have predicted at least 1356 such 2D materials. For all the systems satisfying our criterion, we manually create single layer systems and calculate their energetics, structural, electronic, and elastic properties for both the bulk and the single layer cases. Currently the database consists of 1012 bulk and 430 single layer materials, of which 371 systems are common to bulk and single layer. The rest of calculations are underway. To validate our criterion, we calculated the exfoliation energy of the suggested layered materials, and we found that in 88.9% of the cases the currently accepted criterion for exfoliation was satisfied. Also, using molybdenum telluride as a test case, we performed X-ray diffraction and Raman scattering experiments to benchmark our calculations and understand their applicability and limitations. The data is publicly available at the website http://www.ctcms.nist.gov/{	extasciitilde}knc6/JVASP.html.},
issn={2045-2322},
doi={10.1038/s41598-017-05402-0},
url={https://doi.org/10.1038/s41598-017-05402-0}
}

@misc{choudhary__2018, title={jdft_2d-7-7-2018.json}, url={https://figshare.com/articles/jdft_2d-7-7-2018_json/6815705/1}, DOI={10.6084/m9.figshare.6815705.v1}, abstractNote={2D materials}, publisher={figshare}, author={choudhary, kamal and https://orcid.org/0000-0001-9737-8074}, year={2018}, month={Jul}}




-------------
jarvis_dft_3d
-------------
Various properties of 25,923 bulk materials computed with the OptB88vdW and TBmBJ functionals taken from the JARVIS DFT database.

**Number of entries:** 25923

================= ===============================================================================
Column            Description
================= ===============================================================================
bulk modulus      VRH average calculation of bulk modulus
composition       A Pymatgen Composition descriptor of the composition of the material
e_form            formation energy per atom, in eV/atom
epsilon_x opt     Static dielectric function in x direction calculated with OptB88vDW functional.
epsilon_x tbmbj   Static dielectric function in x direction calculuated with TBMBJ functional.
epsilon_y opt     Static dielectric function in y direction calculated with OptB88vDW functional.
epsilon_y tbmbj   Static dielectric function in y direction calculuated with TBMBJ functional.
epsilon_z opt     Static dielectric function in z direction calculated with OptB88vDW functional.
epsilon_z tbmbj   Static dielectric function in z direction calculuated with TBMBJ functional.
gap opt           Band gap calculated with OptB88vDW functional, in eV
gap tbmbj         Band gap calculated with TBMBJ functional, in eV
jid               JARVIS ID
mpid              Materials Project ID
shear modulus     VRH average calculation of shear modulus
structure         A description of the crystal structure of the material
structure initial Initial structure description of the crystal structure of the material
================= ===============================================================================



**Reference**

3D Dataset discussed in:
Elastic properties of bulk and low-dimensional materials using van der Waals density functional Kamal Choudhary, Gowoon Cheon, Evan Reed, and Francesca Tavazza Phys. Rev. B 98, 014107
Original 3D Data file sourced from:
choudhary, kamal; https://orcid.org/0000-0001-9737-8074 (2018): jdft_3d.json. figshare. Dataset.



**Bibtex Formatted Citations**

@article{PhysRevB.98.014107,
title = {Elastic properties of bulk and low-dimensional materials using van der Waals density functional},
author = {Choudhary, Kamal and Cheon, Gowoon and Reed, Evan and Tavazza, Francesca},
journal = {Phys. Rev. B},
volume = {98},
issue = {1},
pages = {014107},
numpages = {12},
year = {2018},
month = {Jul},
publisher = {American Physical Society},
doi = {10.1103/PhysRevB.98.014107},
url = {https://link.aps.org/doi/10.1103/PhysRevB.98.014107}
}

@misc{choudhary__2018, title={jdft_3d.json}, url={https://figshare.com/articles/jdft_3d-7-7-2018_json/6815699/2}, DOI={10.6084/m9.figshare.6815699.v2}, abstractNote={https://jarvis.nist.gov/
The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.}, publisher={figshare}, author={choudhary, kamal and https://orcid.org/0000-0001-9737-8074}, year={2018}, month={Jul}}




----------------------
jarvis_ml_dft_training
----------------------
Various properties of 24,759 bulk and 2D materials computed with the OptB88vdW and TBmBJ functionals taken from the JARVIS DFT database.

**Number of entries:** 24759

=============== ===============================================================================
Column          Description
=============== ===============================================================================
bulk modulus    VRH average calculation of bulk modulus
composition     A descriptor of the composition of the material
e mass_x        Effective electron mass in x direction (BoltzTraP)
e mass_y        Effective electron mass in y direction (BoltzTraP)
e mass_z        Effective electron mass in z direction (BoltzTraP)
e_exfol         exfoliation energy per atom in eV/atom
e_form          formation energy per atom, in eV/atom
epsilon_x opt   Static dielectric function in x direction calculated with OptB88vDW functional.
epsilon_x tbmbj Static dielectric function in x direction calculated with TBMBJ functional.
epsilon_y opt   Static dielectric function in y direction calculated with OptB88vDW functional.
epsilon_y tbmbj Static dielectric function in y direction calculated with TBMBJ functional.
epsilon_z opt   Static dielectric function in z direction calculated with OptB88vDW functional.
epsilon_z tbmbj Static dielectric function in z direction calculated with TBMBJ functional.
gap opt         Band gap calculated with OptB88vDW functional, in eV
gap tbmbj       Band gap calculated with TBMBJ functional, in eV
hole mass_x     Effective hole mass in x direction (BoltzTraP)
hole mass_y     Effective hole mass in y direction (BoltzTraP)
hole mass_z     Effective hole mass in z direction (BoltzTraP)
jid             JARVIS ID
mpid            Materials Project ID
mu_b            Magnetic moment, in Bohr Magneton
shear modulus   VRH average calculation of shear modulus
structure       A Pymatgen Structure object describing the crystal structure of the material
=============== ===============================================================================



**Reference**

Dataset discussed in:
Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape Kamal Choudhary, Brian DeCost, and Francesca Tavazza Phys. Rev. Materials 2, 083801

Original Data file sourced from:
choudhary, kamal (2018): JARVIS-ML-CFID-descriptors and material properties. figshare. Dataset.



**Bibtex Formatted Citations**

@article{PhysRevMaterials.2.083801,
title = {Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape},
author = {Choudhary, Kamal and DeCost, Brian and Tavazza, Francesca},
journal = {Phys. Rev. Materials},
volume = {2},
issue = {8},
pages = {083801},
numpages = {8},
year = {2018},
month = {Aug},
publisher = {American Physical Society},
doi = {10.1103/PhysRevMaterials.2.083801},
url = {https://link.aps.org/doi/10.1103/PhysRevMaterials.2.083801}
}

@misc{choudhary_2018, title={JARVIS-ML-CFID-descriptors and material properties}, url={https://figshare.com/articles/JARVIS-ML-CFID-descriptors_and_material_properties/6870101/1}, DOI={10.6084/m9.figshare.6870101.v1}, abstractNote={Classical force-field inspired descriptors (CFID) for more than 25000 materials and their material properties such as bandgap, formation energies, modulus of elasticity etc. See JARVIS-ML: https://jarvis.nist.gov/}, publisher={figshare}, author={choudhary, kamal}, year={2018}, month={Jul}}




----
m2ax
----
Elastic properties of 223 stable M2AX compounds from "A comprehensive survey of M2AX phase elastic properties" by Cover et al. Calculations are PAW PW91.

**Number of entries:** 223

=============== ==================================================================================
Column          Description
=============== ==================================================================================
a               Lattice parameter a, in A (angstrom)
bulk modulus    In GPa
c               lattice parameter c, in A (angstrom)
c11             Elastic constants of the M2AX material. These are specific to hexagonal materials.
c12             Elastic constants of the M2AX material. These are specific to hexagonal materials.
c13             Elastic constants of the M2AX material. These are specific to hexagonal materials.
c33             Elastic constants of the M2AX material. These are specific to hexagonal materials.
c44             Elastic constants of the M2AX material. These are specific to hexagonal materials.
d_ma            distance from the M atom to the A atom
d_mx            distance from the M atom to the X atom
elastic modulus In GPa
formula         chemical formula
shear modulus   In GPa
=============== ==================================================================================



**Reference**

http://iopscience.iop.org/article/10.1088/0953-8984/21/30/305403/meta



**Bibtex Formatted Citations**

@article{M F Cover,
author={M F Cover and O Warschkow and M M M Bilek and D R McKenzie},
title={A comprehensive survey of M 2 AX phase elastic properties},
journal={Journal of Physics: Condensed Matter},
volume={21},
number={30},
pages={305403},
url={http://stacks.iop.org/0953-8984/21/i=30/a=305403},
year={2009},
abstract={M 2 AX phases are a family of nanolaminate, ternary alloys that are composed of slabs of transition metal carbide or nitride (M 2 X) separated by single atomic layers of a main group element. In this combination, they manifest many of the beneficial properties of both ceramic and metallic compounds, making them attractive for many technological applications. We report here the results of a large scale computational survey of the elastic properties of all 240 elemental combinations using first-principles density functional theory calculations. We found correlations revealing the governing role of the A element and its interaction with the M element on the c axis compressibility and shearability of the material. The role of the X element is relatively minor, with the strongest effect seen in the in-plane constants C 11 and C 12 . We identify several elemental compositions with extremal properties such as W 2 SnC, which has by far the lowest value of C 44 , suggesting potential applications as a...}}




------
mp_all
------
A complete copy of the Materials Project database as of 10/18/2018. Mp_all files contain structure data for each material while mp_nostruct does not.

**Number of entries:** 83989

================== =========================================================================================
Column             Description
================== =========================================================================================
bulk modulus       in GPa, average of Voight, Reuss, and Hill
e_form             Formation energy per atom (eV)
e_hull             The calculated energy above the convex hull, in eV per atom
elastic anisotropy The ratio of elastic anisotropy.
formula            The chemical formula of the MP entry
gap pbe            The band gap in eV calculated with PBE-DFT functional
initial structure  A Pymatgen Structure object describing the material crystal structure prior to relaxation
mpid               (input): The Materials Project mpid, as a string.
mu_b               The total magnetization of the unit cell.
shear modulus      in GPa, average of Voight, Reuss, and Hill
structure          A Pymatgen Structure object describing the material crystal structure
================== =========================================================================================



**Reference**

A. Jain*, S.P. Ong*, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson (*=equal contributions)
The Materials Project: A materials genome approach to accelerating materials innovation
APL Materials, 2013, 1(1), 011002.
doi:10.1063/1.4812323



**Bibtex Formatted Citations**

@article{Jain2013,
author = {Jain, Anubhav and Ong, Shyue Ping and Hautier, Geoffroy and Chen, Wei and Richards, William Davidson and Dacek, Stephen and Cholia, Shreyas and Gunter, Dan and Skinner, David and Ceder, Gerbrand and Persson, Kristin a.},
doi = {10.1063/1.4812323},
issn = {2166532X},
journal = {APL Materials},
number = {1},
pages = {011002},
title = {{The Materials Project: A materials genome approach to accelerating materials innovation}},
url = {http://link.aip.org/link/AMPADS/v1/i1/p011002/s1\&Agg=doi},
volume = {1},
year = {2013}
}




-----------
mp_nostruct
-----------
A complete copy of the Materials Project database as of 10/18/2018. Mp_all files contain structure data for each material while mp_nostruct does not.

**Number of entries:** 83989

================== ===========================================================
Column             Description
================== ===========================================================
bulk modulus       in GPa, average of Voight, Reuss, and Hill
e_form             Formation energy per atom (eV)
e_hull             The calculated energy above the convex hull, in eV per atom
elastic anisotropy The ratio of elastic anisotropy.
formula            The chemical formula of the MP entry
gap pbe            The band gap in eV calculated with PBE-DFT functional
mpid               (input): The Materials Project mpid, as a string.
mu_b               The total magnetization of the unit cell.
shear modulus      in GPa, average of Voight, Reuss, and Hill
================== ===========================================================



**Reference**

A. Jain*, S.P. Ong*, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson (*=equal contributions)
The Materials Project: A materials genome approach to accelerating materials innovation
APL Materials, 2013, 1(1), 011002.
doi:10.1063/1.4812323



**Bibtex Formatted Citations**

@article{Jain2013,
author = {Jain, Anubhav and Ong, Shyue Ping and Hautier, Geoffroy and Chen, Wei and Richards, William Davidson and Dacek, Stephen and Cholia, Shreyas and Gunter, Dan and Skinner, David and Ceder, Gerbrand and Persson, Kristin a.},
doi = {10.1063/1.4812323},
issn = {2166532X},
journal = {APL Materials},
number = {1},
pages = {011002},
title = {{The Materials Project: A materials genome approach to accelerating materials innovation}},
url = {http://link.aip.org/link/AMPADS/v1/i1/p011002/s1\&Agg=doi},
volume = {1},
year = {2013}
}




--------------------
phonon_dielectric_mp
--------------------
Phonon (lattice/atoms vibrations) and dielectric properties of 1296 compounds computed via ABINIT software package in the harmonic approximation based on density functional perturbation theory.

**Number of entries:** 1296

=============== ======================================================================================================================================================================================================
Column          Description
=============== ======================================================================================================================================================================================================
eps_electronic  A target variable of the dataset, electronic contribution to the calculated dielectric constant; unitless.
eps_total       A target variable of the dataset, total calculated dielectric constant. Unitless: it is a ratio over the dielectric constant at vacuum.
formula         The chemical formula of the material
last phdos peak A target variable of the dataset, the frequency of the last calculated phonon density of states in 1/cm; may be used as an estimation of dominant longitudinal optical phonon frequency, a descriptor.
mpid            The Materials Project identifier for the material
structure       A pymatgen Structure object describing the chemical strucutre of the material
=============== ======================================================================================================================================================================================================



**Reference**

Petretto, G. et al. High-throughput density functional perturbation theory phonons for inorganic materials. Sci. Data 5:180065 doi: 10.1038/sdata.2018.65 (2018).
Petretto, G. et al. High-throughput density functional perturbation theory phonons for inorganic materials. (2018). figshare. Collection.



**Bibtex Formatted Citations**

@Article{Petretto2018,
author={Petretto, Guido
and Dwaraknath, Shyam
and P.C. Miranda, Henrique
and Winston, Donald
and Giantomassi, Matteo
and van Setten, Michiel J.
and Gonze, Xavier
and Persson, Kristin A.
and Hautier, Geoffroy
and Rignanese, Gian-Marco},
title={High-throughput density-functional perturbation theory phonons for inorganic materials},
journal={Scientific Data},
year={2018},
month={May},
day={01},
publisher={The Author(s)},
volume={5},
pages={180065},
note={Data Descriptor},
url={http://dx.doi.org/10.1038/sdata.2018.65}
}

@misc{petretto_dwaraknath_miranda_winston_giantomassi_rignanese_van setten_gonze_persson_hautier_2018, title={High-throughput Density-Functional Perturbation Theory phonons for inorganic materials}, url={https://figshare.com/collections/High-throughput_Density-Functional_Perturbation_Theory_phonons_for_inorganic_materials/3938023/1}, DOI={10.6084/m9.figshare.c.3938023.v1}, abstractNote={The knowledge of the vibrational properties of a material is of key importance to understand physical phenomena such as thermal conductivity, superconductivity, and ferroelectricity among others. However, detailed experimental phonon spectra are available only for a limited number of materials which hinders the large-scale analysis of vibrational properties and their derived quantities. In this work, we perform ab initio calculations of the full phonon dispersion and vibrational density of states for 1521 semiconductor compounds in the harmonic approximation based on density functional perturbation theory. The data is collected along with derived dielectric and thermodynamic properties. We present the procedure used to obtain the results, the details of the provided database and a validation based on the comparison with experimental data.}, publisher={figshare}, author={Petretto, Guido and Dwaraknath, Shyam and Miranda, Henrique P. C. and Winston, Donald and Giantomassi, Matteo and Rignanese, Gian-Marco and Van Setten, Michiel J. and Gonze, Xavier and Persson, Kristin A and Hautier, Geoffroy}, year={2018}, month={Apr}}




--------------------
piezoelectric_tensor
--------------------
941 structures with piezoelectric properties, calculated with DFT-PBE.

**Number of entries:** 941

==================== ==================================================================================================================================
Column               Description
==================== ==================================================================================================================================
cif                  optional: Description string for structure
eij_max              Piezoelectric modulus
formula              Chemical formula of the material
material_id          Materials Project ID of the material
meta                 optional, metadata descriptor of the datapoint
nsites               The \# of atoms in the unit cell of the calculation.
piezoelectric_tensor Tensor describing the piezoelectric properties of the material
point_group          Descriptor of crystallographic structure of the material
poscar               optional: Poscar metadata
space_group          Integer specifying the crystallographic structure of the material
structure            pandas Series defining the structure of the material
v_max                Crystallographic direction
volume               Volume of the unit cell in cubic angstroms, For supercell calculations, this quantity refers to the volume of the full supercell. 
==================== ==================================================================================================================================



**Reference**

de Jong, M., Chen, W., Geerlings, H., Asta, M. & Persson, K. A.
A database to enable discovery and design of piezoelectric materials.
Sci. Data 2, 150053 (2015)



**Bibtex Formatted Citations**

@Article{deJong2015,
author={de Jong, Maarten and Chen, Wei and Geerlings, Henry
and Asta, Mark and Persson, Kristin Aslaug},
title={A database to enable discovery and design of piezoelectric
materials},
journal={Scientific Data},
year={2015},
month={Sep},
day={29},
publisher={The Author(s)},
volume={2},
pages={150053},
note={Data Descriptor},
url={http://dx.doi.org/10.1038/sdata.2015.53}
}




--------------
steel_strength
--------------
312 steels with experimental yield strength and ultimate tensile strength, extracted and cleaned (including de-duplicating) from Citrine.

**Number of entries:** 312

================ ================================
Column           Description
================ ================================
al               weight percent of Al
c                weight percent of C
co               weight percent of Co
cr               weight percent of Cr
elongation       elongation in %
formula          Chemical formula of the entry
mn               weight percent of Mn
mo               weight percent of Mo
n                weight percent of N
nb               weight percent of Nb
ni               weight percent of Ni
si               weight percent of Si
tensile strength ultimate tensile strength in GPa
ti               weight percent of Ti
v                weight percent of V
w                weight percent of W
yield strength   yield strength in GPa
================ ================================



**Reference**

https://citrination.com/datasets/153092/



**Bibtex Formatted Citations**

@misc{Citrine Informatics,
title = {Mechanical properties of some steels},
howpublished = {\url{https://citrination.com/datasets/153092/},
}




----------------
wolverton_oxides
----------------
4,914 perovskite oxides containing composition data, lattice constants, and formation + vacancy formation energies. All perovskites are of the form ABO3. Adapted from a dataset presented by Emery and Wolverton.

**Number of entries:** 4914

================= =======================================================================================
Column            Description
================= =======================================================================================
a                 Lattice parameter a, in A (angstrom)
alpha             Lattice angle alpha, in degrees
atom a            The atom in the 'A' site of the pervoskite.
atom b            The atom in the 'B' site of the perovskite.
b                 Lattice parameter b, in A (angstrom)
beta              Lattice angle beta, in degrees
c                 Lattice parameter c, in A (angstrom)
e_form            Formation energy in eV
e_form oxygen     Formation energy of oxygen vacancy (eV)
e_hull            Energy above convex hull, wrt. OQMD db (eV)
formula           Chemical formula of the entry
gamma             Lattice angle gamma, in degrees
gap pbe           Bandgap in eV from PBE calculations
lowest distortion Local distortion crystal structure with lowest energy among all considered distortions.
mu_b              Magnetic moment
vpa               Volume per atom (A^3/atom)
================= =======================================================================================



**Reference**

Emery, A. A. & Wolverton, C. High-throughput DFT calculations of formation energy, stability and oxygen vacancy formation energy of ABO3 perovskites. Sci. Data 4:170153 doi: 10.1038/sdata.2017.153 (2017).
Emery, A. A., & Wolverton, C. Figshare http://dx.doi.org/10.6084/m9.figshare.5334142 (2017)



**Bibtex Formatted Citations**

@Article{Emery2017,
author={Emery, Antoine A.
and Wolverton, Chris},
title={High-throughput DFT calculations of formation energy, stability and oxygen vacancy formation energy of ABO3 perovskites},
journal={Scientific Data},
year={2017},
month={Oct},
day={17},
publisher={The Author(s)},
volume={4},
pages={170153},
note={Data Descriptor},
url={http://dx.doi.org/10.1038/sdata.2017.153}
}

@misc{emery_2017, title={High-throughput DFT calculations of formation energy, stability and oxygen vacancy formation energy of ABO3 perovskites}, url={https://figshare.com/articles/High-throughput_DFT_calculations_of_formation_energy_stability_and_oxygen_vacancy_formation_energy_of_ABO3_perovskites/5334142/1}, DOI={10.6084/m9.figshare.5334142.v1}, abstractNote={ABO3 perovskites are oxide materials that are used for a variety of applications such as solid oxide fuel cells, piezo-, ferro-electricity and water splitting. Due to their remarkable stability with respect to cation substitution, new compounds for such applications potentially await discovery. In this work, we present an exhaustive dataset of formation energies of 5,329 cubic and distorted perovskites that were calculated using first-principles density functional theory. In addition to formation energies, several additional properties such as oxidation states, band gap, oxygen vacancy formation energy, and thermodynamic stability with respect to all phases in the Open Quantum Materials Database are also made publicly available. This large dataset for this ubiquitous crystal structure type contains 395 perovskites that are predicted to be thermodynamically stable, of which many have not yet been experimentally reported, and therefore represent theoretical predictions. The dataset thus opens avenues for future use, including materials discovery in many research-active areas.}, publisher={figshare}, author={Emery, Antoine}, year={2017}, month={Aug}}




