MLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPS
This provides a tool for researchers in computational physics and materials science to incorporate machine learning into simulations, but it is incremental as it builds on existing simulation frameworks.
The authors introduced MLMOD, a software package that integrates machine learning models into LAMMPS simulations to tackle problems in microscale mechanics and molecular dynamics, enabling data-driven modeling for dynamics, interactions, and system features without specifying concrete numerical results.
MLMOD is a software package for incorporating machine learning approaches and models into simulations of microscale mechanics and molecular dynamics in LAMMPS. Recent machine learning approaches provide promising data-driven approaches for learning representations for system behaviors from experimental data and high fidelity simulations. The package faciliates learning and using data-driven models for (i) dynamics of the system at larger spatial-temporal scales (ii) interactions between system components, (iii) features yielding coarser degrees of freedom, and (iv) features for new quantities of interest characterizing system behaviors. MLMOD provides hooks in LAMMPS for (i) modeling dynamics and time-step integration, (ii) modeling interactions, and (iii) computing quantities of interest characterizing system states. The package allows for use of machine learning methods with general model classes including Neural Networks, Gaussian Process Regression, Kernel Models, and other approaches. Here we discuss our prototype C++/Python package, aims, and example usage. The package is integrated currently with the mesocale and molecular dynamics simulation package LAMMPS and PyTorch. For related papers, examples, updates, and additional information see https://github.com/atzberg/mlmod and http://atzberger.org/.