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• W3134963087 abstract "Predictions of relative stabilities of (competing) molecular crystals are of great technological relevance, most notably for the pharmaceutical industry. However, they present a long-standing challenge for modeling, as often minuscule free energy differences are sensitively affected by the description of electronic structure, the statistical mechanics of the nuclei and the cell, and thermal expansion. The importance of these effects has been individually established, but rigorous free energy calculations for general molecular compounds, which simultaneously account for all effects, have hitherto not been computationally viable. Here we present an efficient end to end framework that seamlessly combines state-of-the art electronic structure calculations, machine-learning potentials, and advanced free energy methods to calculate ab initio Gibbs free energies for general organic molecular materials. The facile generation of machine-learning potentials for a diverse set of polymorphic compounds-benzene, glycine, and succinic acid-and predictions of thermodynamic stabilities in qualitative and quantitative agreement with experiments highlight that predictive thermodynamic studies of industrially relevant molecular materials are no longer a daunting task." @default.
• W3134963087 created "2021-03-15" @default.
• W3134963087 creator A5009821984 @default.
• W3134963087 creator A5042465133 @default.
• W3134963087 date "2022-02-07" @default.
• W3134963087 modified "2023-09-29" @default.
• W3134963087 title "A complete description of thermodynamic stabilities of molecular crystals" @default.
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• W3134963087 doi "https://doi.org/10.1073/pnas.2111769119" @default.
• W3134963087 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35131847" @default.
• W3134963087 hasPublicationYear "2022" @default.
• W3134963087 type Work @default.
• W3134963087 sameAs 3134963087 @default.

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