MLP-Project

This repository serves as the central hub for a machine learning potentials (MLP) project, integrating customized versions of MACE and ASE along with a comprehensive suite of utility scripts for data generation, training, active learning, and analysis. The atomic structures used for training are also included as .xyz files for transparency and reproducibility.

Submodules

This repository includes two modified forks as Git submodules:

• external/mace_node — custom version of MACE
• external/ase_multiproc_calc — modified ASE with multiprocessing calculator support

Scripts Directory Overview

The scripts/ folder organizes all supporting code used throughout the MLP workflow:

• data_generation/ — Scripts and structure files used in model initialization and benchmarking:
• mlp_training/ — Input and analysis scripts for training machine learning potentials:
• nn_ensembles/ — Scripts for analyzing uncertainty vs. error correlation:
• active_learning/ — Scripts to select retraining data based on local uncertainty (spikes):
• uncertainty_analysis/ — Post-MD tools for ensemble uncertainty analysis:

Training Data

The xyz_files/ directory contains the datasets used for training in extended XYZ format. These include the initial seeds and active learning configurations.

Example Simulations

The examples/ directory contains usage examples of ensemble-based simulations and uncertainty-driven sampling:

• ASE_multiproc_calc/: Demonstrates a multiprocessing-enabled ASE interface for evaluating ANN ensemble models (e.g., aenet) on CPUs, enabling fast MD with uncertainty estimation.
• MACE_energybias/: Shows how to perform uncertainty-biased simulations using the MACE ASE calculator by modifying the potential energy with an ensemble uncertainty-based Gaussian bias.