Accelerated Organic Crystal Structure Prediction with Genetic Algorithms and Machine Learning

TL;DR

This paper introduces a high-throughput pipeline combining neural network potentials, genetic algorithms, and machine learning to significantly improve organic crystal structure prediction efficiency and accuracy.

Contribution

The authors develop a novel pipeline that integrates neural network potentials with genetic algorithms for faster, more accurate crystal structure prediction from molecular composition.

Findings

Random search alone matches ~50% of targets.

Genetic algorithm improves matches to ~80%.

ML models trained on small datasets can predict structures with ~60% success.

Abstract

We present a high-throughput, end-to-end pipeline for organic crystal structure prediction (CSP) -- the problem of identifying the stable crystal structures that will form from a given molecule based only on its molecular composition. Our tool uses Neural Network Potentials (NNPs) to allow for efficient screening and structural relaxations of generated crystal candidates. Our pipeline consists of two distinct stages -- random search, whereby crystal candidates are randomly generated and screened, and optimization, where a genetic algorithm (GA) optimizes this screened population. We assess the performance of each stage of our pipeline on 21 molecules taken from the Cambridge Crystallographic Data Centre's CSP blind tests. We show that random search alone yields matches for $\approx 50\%$ of targets. We then validate the potential of our full pipeline, making use of the GA to optimize the Root Mean-Squared Deviation (RMSD) between crystal candidates and the experimentally derived structure. With this approach, we are able to find matches for $\approx80\%$ of candidates with 10-100 times smaller initial population sizes than when using random search. Lastly, we run our full pipeline with an ANI model that is trained on a small dataset of molecules extracted from crystal structures in the Cambridge Structural Database, generating $\approx 60\%$ of targets. By leveraging ML models trained to predict energies at the DFT level, our pipeline has the potential to approach the accuracy of \emph{ab initio} methods and the efficiency of empirical force-fields.