XMFlib 0.4.1

A library for pair-probability and coverage prediction using machine learning.

XMFlib

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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

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Installation

pip install XMFlib

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Virtual Environment Setup (Recommended)

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

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1. PairProbML

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

Basic 1NN example:

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:

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)

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2. CovML

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

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)