Machine learning of lateral adsorbate interactions in surface reaction kinetics. (June 2022)

• Record Type: Journal Article
• Authors: Mou, Tianyou; Han, Xue; Zhu, Huiyuan; Xin, Hongliang
• Is Part Of: Current opinion in chemical engineering. Volume 36(2022)
• Publication Date: 2022-06
• Subjects: Chemical engineering -- Periodicals; Chemical engineering; Periodicals; 660.05
• DOI: 10.1016/j.coche.2022.100825
• Languages: English
• ISSNs: 2211-3398

Abstract

Abstract : The importance of lateral adsorbate interactions cannot be overstated in describing surface reaction kinetics. To realize the goal of operando computational modeling of catalytic processes, it is crucial to integrate effects of relevant adsorbate coverages and configurations into mean-field kinetic analysis and beyond. Herein, we highlight the recent applications of machine learning (ML) algorithms in the development of adsorbate-adsorbate interaction models, ranging from analytic relationships, to ML-parameterized cluster expansions, and to highly nonlinear deep learning models. We also discuss prospects and challenges in moving the field forward, particularly in the integration of theoretical understanding into ML of lateral adsorbate interactions across the chemistry and materials space.