Combinatorial discovery and investigation of the synergism of green amino acid corrosion inhibitors
Integrating high-throughput experiments and interpretable machine learning approach
Jingzhi Yang (Liaoning Academy of Materials, University of Science and Technology Beijing)
Junsen Zhao (Liaoning Academy of Materials, University of Science and Technology Beijing)
Xin Guo (Liaoning Academy of Materials, University of Science and Technology Beijing)
Yami Ran (Liaoning Academy of Materials, University of Science and Technology Beijing)
Zhongheng Fu (University of Science and Technology Beijing, Liaoning Academy of Materials)
Hongchang Qian (University of Science and Technology Beijing, Liaoning Academy of Materials)
Lingwei Ma (Liaoning Academy of Materials, University of Science and Technology Beijing)
Patrick Keil (BASF SE)
J.M.C. Mol (TU Delft - Team Arjan Mol)
Dawei Zhang (University of Science and Technology Beijing, Liaoning Academy of Materials)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
The discovery of synergistic strategies effectively improves the corrosion inhibition capability of amino acids. However, the wide variety of amino acid formulations and the time-consuming nature of corrosion tests make combinatorial discovery challenging to achieve. Herein, a library of 70 amino acids was created and tested in a high-throughput manner. Benefiting from a vast amount of labeled data of amino acid formulations, an interpretable machine learning approach was used to reveal the contribution of molecular features to inhibition performance of amino acids and the synergisms in the optimal formulation. The synergism was verified by electrochemical tests and quantum chemical calculations.