Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen
Yihuang Xiong, Quinn T. Campbell, Julian Fanghanel, Catherine K. Badding, Huaiyu Wang, Nicole E. Kirchner-Hall, Monica J. Theibault, Iurii Timrov, Jared S. Mondschein, Kriti Seth, Rowan R. Katzbaer, Andrés Molina Villarino, Betül Pamuk, Megan E. Penrod, Mohammed M. Khan, Tiffany Rivera, Nathan C. Smith, Xavier Quintana, Paul Orbe, Craig J. Fennie, Senorpe Asem-Hiablie, James L. Young, Todd G. Deutsch, Matteo Cococcioni, Venkatraman Gopalan, Héctor D. Abruña, Raymond E. Schaak, Ismaila Dabo
Abstract
We develop and demonstrate a comprehensive data-driven screening protocol with co-validation between experiment and theory to maximize the success rate of materials discovery for photocatalytic hydrogen generation.
Topics & Concepts
Process engineeringHydrogenHydrogen productionProduction (economics)Environmental scienceProtocol (science)Waste managementBiochemical engineeringMaterials sciencePhotovoltaic systemProduction rateSolar energyComputer scienceNanotechnologyMachine Learning in Materials ScienceAdvanced Photocatalysis TechniquesElectrocatalysts for Energy Conversion