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

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...

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Bibliographic Details
Published in:Nature communications 2023-06, Vol.14 (1), p.3647-3647, Article 3647
Main Authors: Wang, Xiaoqian, Huang, Yang, Xie, Xiaoyu, Liu, Yan, Huo, Ziyu, Lin, Maverick, Xin, Hongliang, Tong, Rong
Format: Article
Language:English
Subjects:
119/118
140/131
140/58
639/638/455/941
639/638/563/980
Addition polymerization
Algorithms
Aluminum
Bayesian analysis
Catalysts
Empirical analysis
Humanities and Social Sciences
Machine learning
Mathematical models
Mechanical properties
Molecular orbitals
multidisciplinary
Optimization
Polymerization
Polymers
Prediction models
Ring opening polymerization
Science
Science (multidisciplinary)
Stereoselectivity
Thermodynamic properties
Workflow
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Summary: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 (%V bur ) and the highest occupied molecular orbital energy ( E HOMO ), 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.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-023-39405-5