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Figure 10. (A) CGCNN process diagram. CGCNN converts crystal structures into feature vectors, learning and predicting material properties. Copyright 2018, American Physical Society, Reproduced with permission^[132]; (B) Process diagram for building predictive models using ML. This performance model holds the capability to forecast catalytic-related properties based on computational data and information sourced from material databases; (C) The general steps of catalyst optimization genetic algorithm supported by AI. Copyright 2024, American Chemical Society, Reproduced with permission^[136]; (D) Roadmap for generating NES; (E) Workflow of Bayesian Optimization algorithm. Copyright 2021, OAE Publishing Inc. Reproduced with permission^[137]; (F) Initial state model with limited data points; (G) Advanced stage of optimization, model improved through a larger dataset; (H) Predicted optimal point by Bayesian optimization algorithm, along with experimental data points obtained. Copyright 2023, American Chemical Society, Reproduced with permission^[141]. CGCNN: Crystal graph convolutional neural network; ML: machine learning; AI: artificial intelligence; NES: neural evolutionary structures.