Metadata-Version: 2.4
Name: XMFlib
Version: 0.1.6
Summary: A library for pair probability 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
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        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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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>=2.6.0
Requires-Dist: numpy>=2.0.2
Dynamic: license-file

# XMFlib

**XMFlib** is a machine learning-based library for predicting pair-site probabilities, designed for surface science and materials simulation. Leveraging pre-trained neural network models, it can quickly predict various types of pair probabilities based on input interaction energy, temperature, and coverage.

---

## Features

- Supports multiple surface types (e.g., 100, 111 facets)
- Built-in multi-layer perceptron (MLP) models for efficient inference
- Simple and user-friendly API, easy to integrate into research and engineering projects
- Compatible with PyTorch, making it easy to extend and customize 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
```

---

## Usage Example

```python
from XMFlib.PairProbML import PairProbPredictor

predictor = PairProbPredictor()
result = predictor.predict(
    facet=100,                  # Facet type, options: '100' or '111'
    interaction_energy=0.2,     # Interaction energy (eV)
    temperature=400,            # Temperature (K)
    main_coverage=0.5           # Main species coverage (0~1)
)
print(result)
# Example output: {'vacancy_pair': 0.12, 'species_pair': 0.34, 'species_vacancy_pair': 0.54}
```
