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 close to those of force fields, can improve the efficiency of the CSP by reducing or eliminating the need for costly DFT calculations. Here, we investigate active learning methods for training MLIPs with CSP datasets. The combination of active learning with the well-developed sampling methods from CSP yields potentials in a highly automated workflow that are relevant over a wide range of the crystal packing space. To demonstrate these potentials, we illustrate efficiently reranking large, diverse crystal structure landscapes to near-DFT accuracy from force field-based CSP, improving the reliability of the final energy ranking. Furthermore, we demonstrate how these potentials can be extended to more accurately model structures far from lattice energy minima through additional on-the-fly training within Monte Carlo simulations.

Crystal Structure Landscape of Diarylethene-Based Crystalline Solids: A Comprehensive CSD Analysis

“…As machine learning and artificial intelligence play an increasingly important role in crystal structure prediction and crystal engineering, − it is critical that these tools be provided with high-quality and complete data. The CSD is an unparalleled repository for small-molecule single-crystal data; however, it is reliant upon entries from the scientific community it serves.…”

Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2

“…In JPC A , the virtual special issue papers span applications to all aspects of chemical dynamics, molecular property prediction, and electronic structure. A large number of contributions within this collection in JPC A address fundamental research into new or adaptation of existing models for applications of ML to physical chemistry spanning many topical areas. − Many of the papers relate to using ML/AI and other data-driven models to enhance methods within physical chemistry. − A number of contributions address the creation or analysis of ground and excited state potential energy surfaces, − while others address dynamics, kinetics, and thermochemistry, a major area of interest within JPC A . − The use of ML to improve accuracy and efficiency in calculation of molecular properties is also addressed in many articles. − …”