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Large-Scale Parameter Estimation for Crystal Structure Prediction. Part 1: Dataset, Methodology, and Implementation.

D H Bowskill¹, B I Tan¹, A Keates²

¹Department of Chemical Engineering, Sargent Centre for Process Systems Engineering and Institute for Molecular Science and Engineering, Imperial College London, London SW7 2AZ, U.K.

Journal of Chemical Theory and Computation

|November 12, 2024

Summary

This summary is machine-generated.

This study introduces a new database and algorithm to improve hybrid ab initio/empirical force-field (HAIEFF) models for crystal structure prediction (CSP). These advancements address bottlenecks in force-field parameterization, enhancing accuracy for polymorph stability analysis.

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Area of Science:

Computational Chemistry
Materials Science
Crystallography

Background:

Crystal structure prediction (CSP) is crucial for identifying stable polymorphs.
Hybrid ab initio/empirical force-field (HAIEFF) models offer a balance of accuracy and computational cost for CSP.
Current methods for fitting the empirical force-field component of HAIEFF models are inefficient and limited, hindering progress.

Purpose of the Study:

To overcome barriers in HAIEFF model development for CSP.
To create a reliable dataset for force-field parameter fitting.
To develop an efficient algorithm for large-scale parameter estimation in CSP force fields.

Main Methods:

Curated a database of 755 organic crystal structures using high-quality DFT-D calculations.
Developed CrystalEstimator, a new algorithm for force-field parameter estimation.
Tested CrystalEstimator on large-scale problems, fitting up to 62 parameters using data from 445 structures.

Main Results:

The curated database provides diverse geometry and energy data suitable for parameter fitting.
CrystalEstimator demonstrates efficient handling of large-scale parameter estimation, surpassing previous limitations.
The developed methods significantly exceed the scale of prior CSP force-field parametrization efforts.

Conclusions:

The new database and CrystalEstimator program provide a strong foundation for HAIEFF model development.
These advancements are expected to significantly improve the accuracy of HAIEFF models in CSP.
This work paves the way for more accurate prediction of polymorph stability.