Bayesian-optimization-assisted discovery of stereoselective aluminum complexes for ring-opening polymerization of racemic lactide

Xiaoqian Wang, Yang Huang, Xiaoyu Xie, Yan Liu, Ziyu Huo, Maverick Lin, Hongliang Xin () and Rong Tong ()
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Xiaoqian Wang: Virginia Polytechnic Institute and State University
Yang Huang: Virginia Polytechnic Institute and State University
Xiaoyu Xie: Virginia Polytechnic Institute and State University
Yan Liu: Virginia Polytechnic Institute and State University
Ziyu Huo: Virginia Polytechnic Institute and State University
Maverick Lin: Virginia Polytechnic Institute and State University
Hongliang Xin: Virginia Polytechnic Institute and State University
Rong Tong: Virginia Polytechnic Institute and State University

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract Stereoselective ring-opening polymerization catalysts are used to produce degradable stereoregular poly(lactic acids) with thermal and mechanical properties that are superior to those of atactic polymers. However, the process of discovering highly stereoselective catalysts is still largely empirical. We aim to develop an integrated computational and experimental framework for efficient, predictive catalyst selection and optimization. As a proof of principle, we have developed a Bayesian optimization workflow on a subset of literature results for stereoselective lactide ring-opening polymerization, and using the algorithm, we identify multiple new Al complexes that catalyze either isoselective or heteroselective polymerization. In addition, feature attribution analysis uncovers mechanistically meaningful ligand descriptors, such as percent buried volume (%Vbur) and the highest occupied molecular orbital energy (EHOMO), that can access quantitative and predictive models for catalyst development.

Date: 2023
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DOI: 10.1038/s41467-023-39405-5

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