Efficiency of training sets for neural network interatomic potentials

Determine whether training sets for neural network interatomic potentials can be made more efficient while achieving similar or better results than training with large amounts of data.

Background

In atomistic machine learning, generating high-quality training data is computationally expensive and larger datasets increase training costs. While bigger datasets can improve generalization for neural network interatomic potentials (NNIPs), the paper questions whether equivalent or superior performance can be achieved with smaller, more efficient training sets.

This uncertainty motivates the development of principled methods to quantify dataset information content and redundancy, with the goal of minimizing dataset size without sacrificing coverage of the relevant configuration space.