Automated High-throughput Organic Crystal Structure Prediction via Population-based Sampling

TL;DR

This paper presents HTOCSP, a Python package that automates high-throughput prediction and screening of organic crystal structures, facilitating systematic analysis of molecular packing and energy landscapes.

Contribution

The paper introduces HTOCSP, a novel Python tool for automated, high-throughput crystal structure prediction and screening of small organic molecules.

Findings

Screened 100 molecules using different sampling strategies.

Analyzed factors influencing crystal energy landscape complexity.

Discussed limitations and future extensions of HTOCSP.

Abstract

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.