J. Sugar

(Invited) Machine Learning-Based Models and Simulations for Accelerated Discovery of Thermochemical Water-Splitting Media

This work demonstrates how machine learning models can accelerate the high-throughput screening of metal oxides’ oxygen defect thermodynamics to identify promising novel TCH candidates and discusses how liquid metal-mediated thermochemical redox can serve as a promising alternative approach due its drastically reduced operating temperatures and promising technoeconomic outlook.