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January 1, 2025Digital DiscoveryOpen Access

Crystal structure prediction of organic molecules by machine learning-based lattice sampling and structure relaxation

Takuya Taniguchi Ryo Fukasawa

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Key Points

Success in predicting crystal structures increased significantly with the new ML workflow.
The method narrows the search space for organic molecules, leading to faster predictions.
The ML-based approach incorporates lattice sampling and structure relaxation for better accuracy.
These findings suggest machine learning can revolutionize predictions in structural chemistry.

Implication

The approach demonstrates enhanced prediction accuracy for organic molecules, indicating machine learning's potential in structural chemistry.

Abstract

An ML-based workflow doubles crystal structure prediction success by narrowing the search space for organic molecules.

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Cite This Study

Taniguchi et al. (2025) studied this question.

synapsesocial.com/papers/68efbd16d61273c8652d827e https://doi.org/10.1039/d5dd00304k

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