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

AI Article Synopsis

  • Energy Ranking Challenge
  • : A major issue in predicting organic molecular crystal structures is accurately ranking various energy levels of potential structures, as higher-level DFT methods, although reliable, are computationally expensive. -
  • Combining Approaches
  • : To handle the computational cost, researchers often use less accurate empirical force fields initially, sometimes paired with machine-learned interatomic potentials (MLIPs) that mimic high-level methods but are cheaper to compute. -
  • Enhanced Efficiency
  • : By utilizing active learning techniques to train MLIPs on CSP data, the researchers created an automated process that allows for efficient reranking of crystal structures to near-DFT accuracy, improving reliability and expanding the modeling capabilities of the structures.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10860135PMC
http://dx.doi.org/10.1021/acs.jpca.3c07129DOI Listing

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