Enhanced Representation-Based Sampling for the Efficient Generation of Datasets for Machine-Learned Interatomic Potentials

TL;DR

This paper introduces ERBS, a new sampling method that efficiently generates diverse datasets for machine-learned interatomic potentials by combining dimensionality reduction and biasing techniques, improving data quality and exploration.

Contribution

ERBS is a novel enhanced sampling approach that automatically identifies collective variables and reconstructs free energy surfaces using short biased trajectories, advancing dataset generation for interatomic potentials.

Findings

High-fidelity free energy surface reconstruction from short biased trajectories

ERBS improves exploration of configurational space compared to traditional methods

Models trained with ERBS data accurately predict self-diffusion coefficients

Abstract

In this work, we present Enhanced Representation-Based Sampling (ERBS), a novel enhanced sampling method designed to generate structurally diverse training datasets for machine-learned interatomic potentials. ERBS automatically identifies collective variables by dimensionality reduction of atomic descriptors and applies a bias potential inspired by the On-the-Fly Probability Enhanced Sampling framework. We highlight the ability of Gaussian moment descriptors to capture collective molecular motions and explore the impact of biasing parameters using alanine dipeptide as a benchmark system. We show that free energy surfaces can be reconstructed with high fidelity using only short biased trajectories as training data. Further, we apply the method to the iterative construction of a liquid water dataset and compare the quality of simulated self-diffusion coefficients for models trained with molecular dynamics and ERBS data. Further, we active-learn models for liquid water with and without enhanced sampling and compare the quality of simulated self-diffusion coefficients. The self-diffusion coefficients closely match those simulated with a reference model at a significantly reduced dataset size. Finally, we compare the sampling behaviour of enhanced sampling methods by benchmarking the mean squared displacements of \ce{BMIM+BF4-} trajectories simulated with uncertainty-driven dynamics and ERBS and find that the latter significantly increases the exploration of configurational space.