Machine-Learning-Assisted Optimization of a Single-Atom Coordination Environment for Accelerated Fenton Catalysis.
basic_science · Level V
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- Record sourced from PubMed, PMID 37440182.
- Also identified by DOI 10.1021/acsnano.3c03610.
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Abstract
Machine learning (ML) algorithms will be enablers in revolutionizing traditional methods of materials optimization. Here, we broaden the use of ML to assist the construction of Fenton-like single-atom catalysts (SACs) by developing a methodology including model building, training, and prediction. Our approach can efficiently extract synthesis parameters that exert a substantial influence on Fenton activity and accurately predict the phenol degradation rate <i>k</i> of SACs with a mean error of ±0.018 min<sup>-1</sup>. The extended synthesis window with accelerated learning enables the realization that the heating temperatures during SAC synthesis significantly influence the Fe-N coordination number, which ultimately dictates their performance. Through ML-guided optimization, a highly efficient SAC dominated by Fe-N<sub>5</sub> sites with exceptional Fenton activity (<i>k</i> = 0.158 min<sup>-1</sup>) is identified. Our work provides an example for ML-assisted optimization of single-atom coordination environments and illuminates the feasibility of ML in accelerating the development of high-performance catalysts.