Materials structure–property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes
Properties can be tailored by tuning composition in high-order composition spaces. For spaces with complex phase behavior, modeling the properties as a function of composition and phase distribution remains a formidable challenge. We present materials structure–property factorization (MSPF) as an approach to automate modeling of such data and identify synergistic phase interactions. MSPF is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks (DRNets) to matrix factorization-based modeling of the representative properties of each phase in a dataset. MSPF is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation, which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity relationships. Comparing the measured photoactivity to a learned model for non-interacting phases, synergistic phase interactions are identified to guide further photoactivity optimization and understanding. MSPF identifies synergistic interactions of a BiVO₄-like phase with both Cu₂V₂O₇-like and CuV₂O₆-like phases, creating avenues for understanding complex photoelectrocatalysts.
Additional Information© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 03 June 2021. Accepted 08 March 2022. Published 05 April 2022. This study is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award DE-SC0020383. Experiments were additionally supported by the Liquid Sunlight Alliance, which is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Fuels from Sunlight Hub under Award Number DE-SC0021266. Data availability. Phase mapping and photocurrent data (inputs) and the basis patterns and modeled photocurrent (outputs) are available at https://data.caltech.edu/records/1983 (https://doi.org/10.22002/D1.1983). Code availability. Source code is available at https://data.caltech.edu/records/1983 (https://doi.org/10.22002/D1.1983). These authors contributed equally: Dan Guevarra, Lan Zhou. Contributions. D.G. implemented the MSPF algorithm. D.G. and L.Z. processed and analyzed the experimental data. L.Z. M.R., and A.S. collected experimental data. D.C. and C.G. performed phase mapping with assistance from L.Z. and J.G.; D.G., L.Z., C.G., and J.G. wrote the manuscript. J.G. conceptualized and supervised the research. The authors declare no competing interests.
Published - s41524-022-00747-1.pdf
Supplemental Material - 41524_2022_747_MOESM1_ESM.pdf