Machine Learning Accelerates Crystallization for Structure Determination
Abstract
ABSTRACT Single‐crystal X‐ray diffraction (SCXRD) is a powerful tool for structural elucidation, but requires high‐quality crystals that are often difficult to obtain. The crystalline mate strategy helps overcome this limitation by facilitating the co‐crystallization of volatile or complex molecules, with few restrictions on size or purity. However, defining its scope of applicability remains challenging, until now requiring experimental trial‐and‐error screening. Here, we demonstrate a machine learning (ML)‐accelerated workflow that rapidly identifies suitable candidates for co‐crystallization. Through feature engineering and workflow optimization, we trained the MCC model, achieving over 95% prediction accuracy. Experimental validation confirmed 114 successful co‐crystals among 120 predicted compounds. The wide structural and functional diversity exhibited highlights the robustness and broad applicability of our strategy, enabling efficient discovery of new structures by SCXRD under standard laboratory conditions.
Article Details
Authors (11)
Cui‐Zhou Luan
State Key Laboratory of Bioactive Molecules and Druggability Assessment College of Chemistry and Materials Science Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry Jinan University Guangzhou P. R. China
Xue‐Zhi Wang
State Key Laboratory of Bioactive Molecules and Druggability Assessment College of Chemistry and Materials Science Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry Jinan University Guangzhou P. R. China
Jian‐Guo Song
State Key Laboratory of Bioactive Molecules and Druggability Assessment College of Chemistry and Materials Science Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry Jinan University Guangzhou P. R. China
Yu Gu
Jing Wu
Ye‐Ting Wang
State Key Laboratory of Bioactive Molecules and Druggability Assessment College of Chemistry and Materials Science Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry Jinan University Guangzhou P. R. China
Jin‐Feng Liang
State Key Laboratory of Bioactive Molecules and Druggability Assessment College of Chemistry and Materials Science Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry Jinan University Guangzhou P. R. China
Jia‐Le Rao
State Key Laboratory of Bioactive Molecules and Druggability Assessment College of Chemistry and Materials Science Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry Jinan University Guangzhou P. R. China
Mo Xie
State Key Laboratory for Flexible Electronics (LoFE)
Jonathan R. Nitschke
Yusuf Hamied Department of Chemistry
Dan Li