| name | mlip_training_ase_calculator |
| description | Training Machine Learning Interatomic Potentials (MLIP) to predict energies and forces, and wrapping as an ASE Calculator. |
MLIP Training & ASE Calculator Wrapping
Use this skill when developing a custom Machine Learning Interatomic Potential (MLIP) and preparing it for molecular dynamics or structural optimization simulations in Python.
Conceptual Framework
Fitting Energies and Forces
Interatomic potentials model the potential energy surface (PES) of a molecular system. To obtain realistic force fields, models must be trained on both total energy $E$ and atomic forces $F$.
- Forces as Gradients: Forces are defined physically as the negative gradient of energy with respect to atomic coordinates:
$$F_i = -\nabla_{R_i} E$$
- Joint Loss Function: The loss function balances both predictions:
$$\text{Loss} = w_E \text{MSE}(E, E_{\text{DFT}}) + w_F \frac{1}{3N} \sum_{i=1}^N \text{MSE}(F_i, F_{i, \text{DFT}})$$
Wrapping with Atomic Simulation Environment (ASE)
ASE is a standard library for atomic-scale simulations. To run molecular dynamics or structural relaxations, a trained PyTorch model must be wrapped into a custom ase.calculators.calculator.Calculator.
The calculator:
- Receives an
ase.Atoms object (coordinates and elements).
- Computes the energy $E(R)$ using a forward pass.
- Computes forces $F = -\nabla_R E$ using PyTorch Autograd.
- Returns values in ASE standard format.
Running the Training and wrapping Script
Run the script mlip_ase_calc.py to train a potential on synthetic data and wrap it into an ASE calculator:
python scripts/mlip_ase_calc.py --epochs 30 --lr 0.01
This script demonstrates energy-force fitting and tests the custom calculator.
