2024/08/16 by Qiang Zhu, Zhu, Qiang, Shinnosuke Hattori +1
Computer Science · Materials Science · #Computational Drug Discovery Methods #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci)
paper · pdf · doi:10.48550/arxiv.2408.08843
openalex publication_date 2024/08/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 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.