Machine-Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes.

ABSTRACT: A primary challenge in organic molecular crystal structure prediction (CSP) is accurately ranking the energies of potential structures. While high-level solid-state density functional theory (DFT) methods allow for mostly reliable discrimination of the low-energy structures, their high computational cost is problematic because of the need to evaluate tens to hundreds of thousands of trial crystal structures to fully explore typical crystal energy landscapes. Consequently, lower-cost but less accurate empirical force fields are often used, sometimes as the first stage of a hierarchical scheme involving multiple stages of increasingly accurate energy calculations. Machine-learned interatomic potentials (MLIPs), trained to reproduce the results of ab initio methods with computational costs cl

SUBMITTER: Butler PWV 

PROVIDER: S-EPMC10860135 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature