Automated High-throughput Organic Crystal Structure Prediction via Population-based Sampling
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908393797582848 |
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| author | Zhu, Qiang Hattori, Shinnosuke |
| author_facet | Zhu, Qiang Hattori, Shinnosuke |
| contents | With advancements in computational molecular modeling and powerful structure search methods, it is now possible to systematically screen crystal structures for small organic molecules. In this context, we introduce the Python package High-throughput Organic Crystal Structure Prediction (HTOCSP), which enables the prediction and screening of crystal packing for small organic molecules in an automated, high-throughput manner. Specifically, we describe the workflow, which encompasses molecular analysis, force field generation, and crystal generation and sampling, all within customized constraints based on user input. We demonstrate the application of \texttt{HTOCSP} by systematically screening organic crystals for 100 molecules using different sampling strategies and force field options. Furthermore, we analyze the benchmark results to understand the underlying factors that influence the complexity of the crystal energy landscape. Finally, we discuss the current limitations of the package and potential future extensions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_08843 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated High-throughput Organic Crystal Structure Prediction via Population-based Sampling Zhu, Qiang Hattori, Shinnosuke Materials Science Computational Physics With advancements in computational molecular modeling and powerful structure search methods, it is now possible to systematically screen crystal structures for small organic molecules. In this context, we introduce the Python package High-throughput Organic Crystal Structure Prediction (HTOCSP), which enables the prediction and screening of crystal packing for small organic molecules in an automated, high-throughput manner. Specifically, we describe the workflow, which encompasses molecular analysis, force field generation, and crystal generation and sampling, all within customized constraints based on user input. We demonstrate the application of \texttt{HTOCSP} by systematically screening organic crystals for 100 molecules using different sampling strategies and force field options. Furthermore, we analyze the benchmark results to understand the underlying factors that influence the complexity of the crystal energy landscape. Finally, we discuss the current limitations of the package and potential future extensions. |
| title | Automated High-throughput Organic Crystal Structure Prediction via Population-based Sampling |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2408.08843 |