Enhanced sampling of robust molecular datasets with uncertainty-based collective variables
Name
034114_1_5.0246178.pdf
Description
Published version
Size
9.64 MB
Format
Adobe PDF
Checksum (MD5)
bb2edffcfde3a6152c6401b97f6b49f0
Author(s)
Tan, Aik Rui
Dietschreit, Johannes CB
Gómez-Bombarelli, Rafael
Date Issued
January 15, 2025
Journal
The Journal of Chemical Physics
Publisher
AIP Publishing
Citation
Aik Rui Tan, Johannes C. B. Dietschreit, Rafael Gómez-Bombarelli; Enhanced sampling of robust molecular datasets with uncertainty-based collective variables. J. Chem. Phys. 21 January 2025; 162 (3): 034114.
Version
Final published version
Abstract
Generating a dataset that is representative of the accessible configuration space of a molecular system is crucial for the robustness of machine-learned interatomic potentials. However, the complexity of molecular systems, characterized by intricate potential energy surfaces, with numerous local minima and energy barriers, presents a significant challenge. Traditional methods of data generation, such as random sampling or exhaustive exploration, are either intractable or may not capture rare, but highly informative configurations. In this study, we propose a method that leverages uncertainty as the collective variable (CV) to guide the acquisition of chemically relevant data points, focusing on regions of configuration space where ML model predictions are most uncertain. This approach employs a Gaussian Mixture Model-based uncertainty metric from a single model as the CV for biased molecular dynamics simulations. The effectiveness of our approach in overcoming energy barriers and exploring unseen energy minima, thereby enhancing the dataset in an active learning framework, is demonstrated on alanine dipeptide and bulk silica.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Creative Commons Attribution-Noncommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1063/5.0246178
