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<p>Predicting the outcome of a crystallization process remains a long-standing challenge in solid state chemistry. This stems from the subtle interplay between thermodynamics and kinetics that results in a complex crystal energy landscape, spanned by many polymorphs and other metastable intermediates. Molecular simulations are uniquely positioned to unravel this interplay, as they constitute a framework that can compute free energies (thermodynamics), barriers (kinetics), and visualize the crystallization mechanisms at high resolution. We show here how recent progress in computational methods, and their augmentation with Machine Learning, has advanced our ability to predict crystal structure and simulate crystal nucleation.</p><p></p>

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Deciphering the complexities of crystalline state(s) with molecular simulations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Caroline Desgranges,
  • Jerome Delhommelle

摘要

Predicting the outcome of a crystallization process remains a long-standing challenge in solid state chemistry. This stems from the subtle interplay between thermodynamics and kinetics that results in a complex crystal energy landscape, spanned by many polymorphs and other metastable intermediates. Molecular simulations are uniquely positioned to unravel this interplay, as they constitute a framework that can compute free energies (thermodynamics), barriers (kinetics), and visualize the crystallization mechanisms at high resolution. We show here how recent progress in computational methods, and their augmentation with Machine Learning, has advanced our ability to predict crystal structure and simulate crystal nucleation.