Metadata-Version: 2.4
Name: XMFlib
Version: 0.4.1
Summary: A library for pair-probability and coverage prediction using machine learning.
Author-email: Minhui Li <23210220016@m.fudan.edu.cn>
License: MIT License
        
        Copyright (c) 2025 aFui233
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
Project-URL: Homepage, https://github.com/aFui233/XMFlib
Project-URL: Repository, https://github.com/aFui233/XMFlib
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENCE
Requires-Dist: torch>=2.4.0
Requires-Dist: numpy>=2.0.2
Dynamic: license-file

# XMFlib

[English](./README.md) | [中文](./README_CN.md)

**XMFlib** is a lightweight ML inference library for surface science.

It focuses on three core topics:

1. **PairProbML**: pair-site probability prediction
2. **CovML**: coverage prediction
3. **Quick integration**: simple API and pretrained models

---

## Installation

```bash
pip install XMFlib
```

---

## Virtual Environment Setup (Recommended)

```bash
conda create --name <env_name> python=3.9
conda activate <env_name>
pip install XMFlib
```

---

## 1. PairProbML

- Predict pair probabilities on `100` / `111` facets
- Supports 1NN and 2NN inference
- Output order: `[Pee, Paa, Pae]`

Basic 1NN example:

```python
from XMFlib.PairProbML import PairProbPredictor

predictor = PairProbPredictor()
result = predictor.predict(
    facet=100,
    interaction_energy=0.3,
    temperature=400,
    main_coverage=0.7
)
print("Predicted probabilities:", result)
```

2NN example:

```python
from XMFlib.PairProbML import PairProbPredictor

predictor = PairProbPredictor()
result_2nn = predictor.predict_2nn(
    facet=111,
    interaction_energy_1nn=0.16,
    interaction_energy_2nn=0.04,
    temperature=525,
    main_coverage=0.7
)
print("Predicted 2NN probabilities:", result_2nn)
```

---

## 2. CovML

- Predict coverage from interaction energy, adsorption energy, and temperature
- Output order: `[A_Coverage, E_Coverage]`

```python
from XMFlib.CovML import CovPredictor

predictor = CovPredictor()
result = predictor.predict(
    facet=100,
    interaction_energy=0.18,
    adsorption_energy=-0.86,
    temperature=400
)
print("Predicted coverage:", result)
```
