Contribution to: From spin-MLIP's open code to a callable magnetic model

Overview

spin-MLIP is an open-source project that provides machine learning interatomic potentials (MLIPs) for magnetic materials. This post describes how to transform the open code into a callable magnetic model for practical applications.

About spin-MLIP

spin-MLIP extends traditional MLIPs to include magnetic interactions, enabling:

• Accurate modeling of magnetic materials

• Prediction of magnetic properties

• Simulation of magnetic phase transitions

Transformation Steps

1. Environment Setup

git clone https://github.com/spin-mlip/spin-mlip.git
cd spin-mlip
pip install -r requirements.txt

2. Model Training

from spin_mlip import Trainer

# Load training data
trainer = Trainer(
    data_path="magnetic_data.json",
    model_type="graph_neural_network",
    magnetic_features=True
)

# Train the model
trainer.train(epochs=100)
trainer.save("magnetic_model.pt")

3. Model Inference

from spin_mlip import MagneticPredictor

# Load trained model
predictor = MagneticPredictor("magnetic_model.pt")

# Predict magnetic properties
result = predictor.predict("Fe3O4.cif")
print(f"Magnetic moment: {result.magnetic_moment} mu_B")
print(f"Curie temperature: {result.curie_temperature} K")

Applications

• High-throughput screening of magnetic materials

• Design of rare-earth-free permanent magnets

• Study of magnetic phase transitions

Validation

The model has been validated on:

• Known magnetic structures (Fe, Ni, Co)

• Magnetic oxides (Fe3O4, NiO)

• Rare-earth magnets (Nd2Fe14B)

Future Work

• Extend to more complex magnetic systems

• Improve accuracy for rare-earth materials

• Integrate with Ouro platform

This post is submitted as part of the "From spin-MLIP's open code to a callable magnetic model" quest on Ouro.