(558ac) Discover Perovskites for Electrocatalytic Water Oxidation Via Adaptive Machine Learning
AIChE Annual Meeting
2019
2019 AIChE Annual Meeting
Poster Sessions
General Poster Session II
Wednesday, November 13, 2019 - 3:30pm to 5:00pm
In this talk, we demonstrated a probabilistic adaptive machine learning approach to navigate through a rich chemical space ~4000 double perovskites quickly for identifying the stabilized cubic structures with promising OER catalytic activity within the established descriptor-based kinetic framework. Notably, our model exhibits high predictive reliability for predicting the adsorption properties (e.g., *O and *OH), which is attributed to a heterogeneous set of descriptors with multi-fidelity informatics, i.e., compositional and electronic structure descriptor, and an adaptive learning process. By iteratively selecting the appropriate candidates for DFT validations where the Gaussian process regression model is most uncertain about in terms of the OER over potential, we are able to improve the model progressively by expending the training datasets toward as much chemical space as possible with the smallest computational effort. Our model successfully identifies most of the recognized perovskites along with ~50 potential candidates that have not been reported in the previous studies. Moreover, by interpreting the perovskitesâ probability distributions with Kullback-Leibler (KL) divergence analysis, we draw important molecular orbital insight that the metal B site eg orbitals of the perovskite bulk electronic structure is a significant underlying descriptor that governs the OER activity.
References
[1] Z. Li, S. Wang, W.S. Chin, L.E. Achenie, H. Xin, High-throughput screening of bimetallic catalysts enabled by machine learning, J. Mater. Chem. A Mater. Energy Sustain. (2017). doi:10.1039/C7TA01812F.