(Invited) Machine Learning-Based Models and Simulations for Accelerated Discovery of Thermochemical Water-Splitting Media
TL;DR
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.
Abstract
Thermochemical hydrogen (TCH) via water-splitting provides a promising technology pathway for hydrogen production since it, in contrast to electrolysis, does not depend primarily on redirecting electricity from the grid for fuel production. Especially due to recent commercialization efforts, 2-step thermal redox cycles in non-stoichiometric metal oxides are of particularly high interest for this pathway; however, state-of-the-art CeO 2 has several practical limitations, which has motivated continued materials discovery efforts in this field. Our first contribution demonstrates how machine learning models can accelerate the high-throughput screening of metal oxides’ oxygen defect thermodynamics to identify promising novel TCH candidates. Upon their experimental validation, some materials exhibit TCH capabilities comparable to CeO 2 under certain reactor operating conditions. Shifting gears, we then discuss how liquid metal-mediated thermochemical redox can serve as a promising alternative approach due its drastically reduced operating temperatures and promising technoeconomic outlook. Here, machine learned interatomic potentials are instead utilized, accelerating the molecular dynamics simulations needed to understand the phenomena and design rules underpinning their excellent water-splitting capabilities. Sandia National Laboratories is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.