Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials: Data

This repository includes data sets from the paper "Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials". It also provides configuration files for experiments, which can be used to reproduce the results of the paper combined with publicly available code at GitHub.

Data structure for alanine dipeptide: The initial structure (ala2-ffs/ala2_init.extxyz) and the AMBER ff19SB files (ala2-ffs/input.pdb and ala2-ffs/parm7.prmtop) for reference calculations are in the respective folder. We also include the test data set generated by running molecular dynamics simulations at 1200 K using canonical (NVT) statistical ensemble (ala2-ffs/ala2_test.extxyz). Task folders like ala2-ffs/ala2-300K-ffs contain experiments under different conditions (different temperatures). For detailed setups, refer to alebrew/task_execution.py on GitHub, where each task has the same name as the respective folder. Each task includes various methods for learning potentials, with method naming convention as _-_-_-_-. For , parameters denote force biasing and relative hydrogen biasing. Compare method names with respective configuration files. Note we also include adversarial training experiments, i.e., is 'adversarial', along with uncertainty-biased molecular dynamics.

Data structure for MIL-53(Al): The initial structure (mil53/mil53_cp_init.extxyz) and CP2K input file (mil53/mof_base_input.txt) for reference calculations are in the respective folder. Task folders like mil53/mil53_cp-300K-0bar-v2 and mil53/mil53_cp-600K-2500bar-v1 contain experiments under different conditions (different temperatures and pressures). v1 signifies initiation from 32 randomly displaced configurations, while v2 denotes experiments using a pre-trained mode with 256 configurations from Zenodo. For detailed setups, refer to alebrew/task_execution.py on GitHub, where each task has the same name as the respective folder. Each task includes various methods for learning potentials, with method naming convention as _-_-_-_-. For , parameters denote force biasing, relative hydrogen biasing, and stress biasing strengths. Compare method names with respective configuration files.