Whether organic chemists are working to develop new molecular energy or to create new hit drugs in the pharmaceutical industry, each seeks to optimize the chemical structure of the molecules to achieve the desired target properties.
Part of this optimization includes the molecular crystal packaging motif, the perceptual pattern of molecule orientation relative to the crystal structure. Current data sets on packaging motifs have remained small due to intensive manual labeling processes and insufficient labeling schemes.
To help solve this problem, a team of materials from the Lawrence Livermore National Laboratory (LLNL) and computer scientists have developed a free Autopack package that formalizes the packaging motifing process and can automatically process and label packaging motifs on thousands of molecular crystal structures. The research appears in Journal of Chemical Information and Modeling.
Small-scale engineering studies over the past 30 years suggest that although predicting experimental crystal structures from chemical structure alone remains unattainable, there may be relationships between the chemical structure of molecules and the specific crystal structure attribute they adopt, called the packaging motif.
The molecular crystal packaging motif is an important concept for application in energy and organic electronics because of the observed correlations between molecular crystal packaging motifs and properties properties of interest, including insensitivity to molecular explosives and charge transport for molecular semiconductors.
So far, a formalized open source method for assigning packaging motives has never been created. Instead, packaging motifs are attributed to molecular crystals simply by human assessment of crystal structure and judgment, resulting in small and noisy data sets.
“In the era of machine learning, the ability to create large, labeled data sets of molecular crystal packaging motifs is now particularly important,” said LLNL data scientist Donald Loveland, lead author of the article. “Such efforts can create models that can predict packaging motifs only from the chemical structure of the molecules, which would help organic chemists prioritize the synthesis of new molecules based on the desired packaging motif and properties.”
The new LLNL work uses an efficient optimization algorithm that bypasses many of the problems found in the previously proposed packaging motif labeling methods, leading to new superior results when tested on a data set curated by LLNL.
Through Autopack, the researchers were able to generate a dataset of nearly 10,000 packaging motifs for a set of energy and energy-like molecules of interest to the Laboratory, which would have been an impossible task sooner. For context, the literature to date has remained limited to a hundred molecules due to the tedious and time-consuming nature of manual marking. Early analysis of this new data set suggests complex trends between intermolecular interactions, 3-D molecular conformations, and adopted packaging motifs that are currently unexplored in the field, providing guidance on next steps for engineering crystal pipelines.
The code is freely available through the Laboratory Office for Innovation and Partnerships.
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Donald Loveland et al. Automated identification of molecular crystal packaging motifs, Journal of Chemical Information and Modeling (2020). DOI: 10.1021 / acs.jcim.0c01134
Provided by Lawrence Livermore National Laboratory
Citation: Molecular crystal structures pack it (2020, December 24) retrieved December 24, 2020 from https://phys.org/news/2020-12-molecular-crystal.html
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