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From: Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development
Figure 1. Schematic diagram of ML-driven closed-loop catalyst discovery consisting of collecting data, building datasets, training ML models, and predicting materials’ properties to accelerate materials optimization. XAI and PIML approaches enable the interpretation of physical and chemical insights from the “black box” ML models. Reproduced with permission from refs[74,79,81,93]. Copyright 2021 Springer Nature, licensed under Creative Commons CC BY, copyright 2021 by the author(s) and licensed under Creative Commons CC BY, respectively. ML: Machine learning; XAI: explainable artificial intelligence; explainable artificial intelligence; PIML: physics-informed machine learning.
