mcal.calculations package¶

Submodules¶

mcal.calculations.hopping_mobility_model module¶

hopping_mobility_model.py (2025/10/06)

mcal.calculations.hopping_mobility_model.cal_pinv(array: ndarray[Any, dtype[float64]], rcond: float = 0.001) → ndarray[Any, dtype[float64]]¶

Calculate pseudo-inverse matrix using eigenvalue decomposition

Parameters:

array (NDArray[np.float64]) – Input matrix
rcond (float, optional) – Cutoff for small singular values, by default 1e-9

Returns:

Pseudo-inverse matrix

Return type:

NDArray[np.float64]

Raises:

ValueError – The last eigenvalue is not zero.
ValueError – All eigenvalues except the last one should be negative.

mcal.calculations.hopping_mobility_model.demo()¶

mcal.calculations.hopping_mobility_model.diffusion_coefficient_tensor(lattice: ndarray[Any, dtype[float64]], hop: List[Tuple[int, int, int, int, int, float]]) → ndarray[Any, dtype[float64]]¶

Calculate diffusion coefficient tensor from hopping rate

Parameters:

lattice (3x3 numpy.array) – lattice[0,:] is a-axis vector, lattice[1,:] b-axis vector, lattice[2,:] c-axis vector
hop (list of (int, int, int, int, int, float) tuple.) – (s, t, i, j, k, p) means that the hopping rate from s-th molecule in (0, 0, 0) cell to t-th molecule in (i, j, k) cell is p.

Returns:

Diffusion coefficient tensor

Return type:

3x3 numpy.array

mcal.calculations.hopping_mobility_model.diffusion_coefficient_tensor_MC(lattice: ndarray[Any, dtype[float64]], hop: List[Tuple[int, int, int, int, int, float]], steps: int = 100, particles: int = 10000) → ndarray[Any, dtype[float64]]¶

Calculate diffusion coefficient tensor from Monte Carlo simulation using Gillespie algorithm.

Parameters:

lattice (3x3 numpy.array) – lattice[0,:] is a-axis vector, lattice[1,:] b-axis vector, lattice[2,:] c-axis vector
hop (list of (int, int, int, int, int, float) tuple.) – (s, t, i, j, k, p) means that the hopping rate from s-th molecule in (0, 0, 0) cell to t-th molecule in (i, j, k) cell is p.
steps (int) – Number of steps
particles (int) – Number of particles

Returns:

Diffusion coefficient tensor

Return type:

3x3 numpy.array

mcal.calculations.hopping_mobility_model.diffusion_coefficient_tensor_ODE(lattice: ndarray[Any, dtype[float64]], hop: List[Tuple[int, int, int, int, int, float]], max_steps: int = 200, size: int = 40, max_rate: float = 0.05) → ndarray[Any, dtype[float64]]¶

Calculate diffusion coefficient tensor from numerical solution of Ordinary Differential Equation (ODE)

Parameters:

lattice (3x3 numpy.array) – lattice[0,:] is a-axis vector, lattice[1,:] b-axis vector, lattice[2,:] c-axis vector
hop (list of (int, int, int, int, int, float) tuple.) – (s, t, i, j, k, p) means that the hopping rate from s-th molecule in (0, 0, 0) cell to t-th molecule in (i, j, k) cell is p.
max_steps (int) – Maximum number of steps
size (int) – Size of the simulation box
max_rate (float) – Maximum rate of hopping

Returns:

Diffusion coefficient tensor

Return type:

3x3 numpy.array

mcal.calculations.hopping_mobility_model.marcus_rate(transfer: float, reorganization: float, T: float = 300.0) → float¶

Calculate hopping rate (1/s) from transfer integral (eV) and reorganization energy (eV)

Parameters:

transfer (float) – Transfer integral [eV]
reorganization (float) – Reorganization energy [eV]
T (float) – Temperature [K], by default 300.0

Returns:

Hopping rate [1/s]

Return type:

float

mcal.calculations.hopping_mobility_model.mobility_tensor(D: ndarray[Any, dtype[float64]], T: float = 300.0) → ndarray[Any, dtype[float64]]¶

Calculate mobility tensor from diffusion coefficient tensor

Parameters:

D (3x3 numpy.array) – Diffusion coefficient tensor
T (float) – Temperature [K], by default 300.0

Returns:

Mobility tensor

Return type:

3x3 numpy.array

mcal.calculations.hopping_mobility_model.print_tensor(tensor: ndarray[Any, dtype[float64]], msg: str = 'Mobility tensor')¶

mcal.calculations.rcal module¶

Rcal

exception mcal.calculations.rcal.GausTermError¶

Bases: Exception

Exception for Gaussian error termination.

exception mcal.calculations.rcal.OSCTypeError¶

Bases: Exception

Exception for organic semiconductor type.

class mcal.calculations.rcal.Rcal(gjf_file: str, osc_type: Literal['p', 'n'] = 'p')¶

Bases: object

Calculate organization energy.

calc_reorganization(gau_com: str = 'g16', only_read: bool = False, is_output_detail: bool = False, skip_specified_cal: List[Literal['opt_neutral', 'opt_ion', 'neutral', 'ion']] = []) → float¶

Calculate reorganization energy.

Parameters:

gau_com (str) – Gaussian command, by default ‘g16’.
only_read (bool) – if True, the calculation is only read, by default False.
is_output_detail (bool) – if True, the calculation detail will be output, by default False.
skip_specified_cal (List[Literal['opt_neutral', 'opt_ion', 'neutral', 'ion']]) – if specified, the calculation of the specified type will be skipped, by default [].

Returns:

reorganization energy [eV].

Return type:

float

static check_error_term(line: str) → None¶

Check the error term of Gaussian.

Parameters:: line (str) – last line of the log file.
Raises:: GausTermError – if the calculation of Gaussian was error termination.

check_extension_log(gjf: str) → None¶

Check the extension of log file.

Parameters:: gjf (str) – gjf file name.

extract_energy(gjf: str, only_read: bool = False, is_output_detail: bool = False, skip_cal: bool = False) → float¶

Extract energy from log file.

Parameters:

gjf (str) – gjf file name.
only_read (bool) – if True, the calculation is only read, by default False.
is_output_detail (bool) – if True, the calculation detail will be output, by default False.

Returns:

total energy.

Return type:

float

static print_timestamp() → None¶: Print timestamp.

mcal.calculations.rcal.main()¶: This code is to execute rcal for command line.

mcal.calculations.tcal module¶

Tcal

class mcal.calculations.tcal.PairAnalysis(apta)¶

Bases: object

Analyze atomic pair transfer integrals.

print_element_pairs()¶: Print element pairs.

print_largest_pairs()¶: Print largest pairs.

class mcal.calculations.tcal.Tcal(file, monomer1_atom_num=-1)¶

Bases: object

Calculate transfer integrals.

EV = 27211.38456571948¶

atomic_pair_transfer_analysis(analyze_orbital='HOMO', output_apta=False)¶

Calculate atomic pair transfer integrals.

Parameters:

analyze_orbital (str, optional) – Analyze orbital., default ‘HOMO’
output_apta (bool, optional) – If it is True, output csv file of atomic pair transfer integrals., default False

static cal_transfer_integrals(bra, overlap, fock, ket)¶

Calculate intermolecular transfer integrals.

Parameters:

bra (numpy.array) – MO coefficients of one molecule.
overlap (numpy.array) – Overlap matrix of dimer.
fock (numpy.array) – Fock matrix of dimer.
ket (numpy.array) – MO coefficients of the other molecule.

Returns:

Intermolecular transfer integrals.

Return type:

double

check_extension_log()¶: Check the extension of log file.

static check_normal_termination(reader)¶

Whether the calculation of gaussian was successful or not.

Parameters:: reader (_io.TextIOWrapper) – Return value of open function.
Returns:: Return value of function.
Return type:: _io.TextIOWrapper

Examples

>>> with open('sample.log', 'r') as f:
...     f = Tcal.check_normal_termination(f)

convert_xyz_to_gjf(function='B3LYP/6-31G(d,p)', nprocshared=4, mem=16, unit='GB')¶

Convert xyz file to gjf file.

Parameters:

function (str, optional) – _description_, default ‘b3lyp/6-31g(d,p)’
nprocshared (int, optional) – The number of nprocshared., default 4
mem (int, optional) – The number of memory., default 16
unit (str, optional) – The unit of memory., default ‘GB’

create_cube_file()¶: Create cube file.

create_monomer_file()¶: Create gjf files of monomer from gjf file of dimer.

custom_atomic_pair_transfer_analysis(analyze_orb1, analyze_orb2, output_apta=False)¶

Calculate atomic pair transfer integrals.

Parameters:

analyze_orb1 (int, optional) – Analyze orbital., default -1
analyze_orb2 (int, optional) – Analyze orbital., default -1
output_apta (bool, optional) – If it is True, output csv file of atomic pair transfer integrals., default False

static extract_coordinates(reader)¶

Extract coordinates from gjf file of dimer.

Parameters:

reader (_io.TextIOWrapper) – Return value of open function.

Returns:

_io.TextIOWrapper – Return value of open function.
list – The list of coordinates.

Examples

>>> import re
>>> with open(f'sample.gjf', 'r') as f:
...     while True:
...         line = f.readline()
...         if not line:
...             break
...         if re.search('[-0-9]+ [0-3]', line):
...             f, coordinates = Tcal.extract_coordinates(f)

static extract_num(pattern, line, idx=0)¶

Extract integer in strings.

Parameters:

pattern (str) – Strings using regular expression.
line (str) – String of target.

Returns:

If there is integer, return it.

Return type:

int or None

static output_csv(file_name, array)¶

Output csv file of array.

Parameters:

file_name (str) – File name including extension.
array (array_like) – Array to create csv file.

print_apta(a_transfer, message='Atomic Pair Transfer Analysis')¶

Create list of apta and print it.

Parameters:

a_transfer (numpy.array) – Result of atomic pair transfer analysis.
message (str, optional) – Message to print., default ‘Atomic Pair Transfer Analysis’

Returns:

The array of atomic pair transfer analysis.

Return type:

numpy.array

static print_matrix(matrix)¶

Print matrix.

Parameters:: matrix (array_like)

static print_timestamp()¶: Print timestamp.

print_tranfer_integral_diff_levels(nlevel, output_ti_diff_levels=False)¶

print_transfer_integrals()¶: Print transfer integrals of NLUMO, LUMO, HOMO and NHOMO.

read_dimer(is_matrix=False, output_matrix=False)¶

Extract overlap and fock matrix from log file of dimer.

Parameters:

is_matrix (bool, optional) – If it is True, print overlap and fock matrix., default False
output_matrix (bool, optional) – If it is True, Output overlap and fock matrix., default False

static read_matrix(reader, n_basis, n_bsuse)¶

Read matrix.

Parameters:

reader (_io.TextIOWrapper) – Return value of open function.
n_basis (int) – The number of row.
n_bsuse (int) – The number of column.

Returns:

Read matrix like MO coefficients.

Return type:

numpy.array

read_monomer1(is_matrix=False, output_matrix=False)¶

Extract MO coefficients from log file of monomer.

Parameters:

is_matrix (bool, optional) – If it is True, print MO coefficients., default False
output_matrix (bool, optional) – If it is True, Output MO coefficients., default False

read_monomer2(is_matrix=False, output_matrix=False)¶

Extract MO coefficients from log file of monomer.

Parameters:

is_matrix (bool, optional) – If it is True, print MO coefficients., default False
output_matrix (bool, optional) – If it is True, Output MO coefficients., default False

static read_symmetric_matrix(reader, n_basis)¶

Read symmetric matrix.

Parameters:

reader (_io.TextIOWrapper) – Return value of open function.
n_basis (int) – The number of column or row.

Returns:

Read symmetrix matrix like overlap or fock matrix.

Return type:

numpy.array

run_gaussian(gaussian_command, skip_monomer_num=[0])¶

Execute gjf files using gaussian.

Parameters:

gaussian_command (str) – Command of gaussian.
skip_monomer_num (list[int], optional) – If it is 1, skip 1st monomer calculation. If it is 2, skip 2nd monomer calculation. If it is 3, skip dimer calculation.

Returns:

Returncode of subprocess.run.

Return type:

int

mcal.calculations.tcal.main()¶: This code is to execute tcal for command line.

mcal.calculations package¶

Submodules¶

mcal.calculations.hopping_mobility_model module¶

mcal.calculations.rcal module¶

mcal.calculations.tcal module¶

Module contents¶