Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials
Abstract
trajectories simulated with uncertainty-driven dynamics and ERBS and find that the latter significantly increases the exploration of configurational space.
Fields of Study & Subjects
Machine Learning in Materials Science
Protein Structure and Dynamics
Advanced Physical and Chemical Molecular Interactions
Publication details
- Journal / source
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Volume
- 22
- Issue
- 4
- Pages
- 1947–1957
- Publication date
- 2 February 2026
- Language
- EN