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- W3103242512 abstract "We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory (DFT) is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor (RCD) developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the Rigorous workflow is an exhaustive exploration of the potential energy landscape, the Energy workflow produces a set of low energy structures, and the Diverse workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases." @default.
- W3103242512 created "2020-11-23" @default.
- W3103242512 creator A5024866637 @default.
- W3103242512 creator A5034844875 @default.
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- W3103242512 creator A5084126442 @default.
- W3103242512 creator A5088341989 @default.
- W3103242512 date "2018-03-15" @default.
- W3103242512 modified "2023-10-18" @default.
- W3103242512 title "Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation" @default.
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