Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic...

Area of Science:

• Computational Chemistry
• Materials Science
• Machine Learning

Background:

• Machine-learned interatomic potentials require large, diverse datasets for accuracy.
• Current methods for generating such data can be computationally expensive and time-consuming.

Purpose of the Study:

• Introduce Enhanced Representation-based Sampling (ERBS), a novel method for generating high-quality training data.
• Demonstrate ERBS's ability to capture molecular motions and reconstruct free energy surfaces.
• Evaluate ERBS's performance in generating data for liquid water simulations and compare it with traditional methods.

Main Methods:

• ERBS utilizes dimensionality reduction of atomic descriptors to identify collective variables.
• A bias potential, inspired by On-the-Fly probability enhanced sampling, is applied.
• Gaussian moment descriptors are employed to capture collective molecular motions.

Main Results:

• ERBS successfully reconstructs free energy surfaces with high fidelity from short biased trajectories.
• Models trained with ERBS data for liquid water yield self-diffusion coefficients comparable to those from larger, traditional datasets.
• ERBS significantly enhances the exploration of configurational space compared to uncertainty-driven dynamics.

Conclusions:

• ERBS provides an efficient and effective approach for generating structurally diverse training data for machine-learned interatomic potentials.
• The method accelerates the development of accurate molecular models by reducing data generation costs.
• ERBS offers a promising strategy for advancing molecular simulations and materials discovery.