Thomas Young Centre

Distilling First-Principles Accuracy into Compact Machine Learning Potentials for Condensed-Phase Chemistry

Abstract Accurate machine learning interatomic potentials (MLIPs) have made first-principles-quality potential energy surfaces increasingly accessible for condensed-phase chemistry, but their inference cost can still limit the sampling needed to compute experimentally relevant ob...

Improving the accuracy of fixed-node diffusion Monte Carlo for non-covalent interactions

Diffusion quantum Monte Carlo (DMC) and coupled cluster theory [CCSD(T)] are widely used benchmark methods for noncovalent interactions (NCIs). However, recent studies have reported notable discrepancies for several hydrogen-bonded and dispersion-dominated systems, raising questi...