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- Title
Bayesian-optimization-assisted discovery of stereoselective aluminum complexes for ring-opening polymerization of racemic lactide.
- Authors
Wang, Xiaoqian; Huang, Yang; Xie, Xiaoyu; Liu, Yan; Huo, Ziyu; Lin, Maverick; Xin, Hongliang; Tong, Rong
- 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. Stereoselective catalysts impact polymer's properties, but discovering such catalysts is expensive and based on trial-and-error. Here, the authors develop a machine-learning tool to guide catalyst discovery and reveal mechanistic features affecting stereoselectivity.
- Subjects
RING-opening polymerization; FRONTIER orbitals; ALUMINUM
- Publication
Nature Communications, 2023, Vol 14, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-023-39405-5