(659g) Large-Scale Exploration of Perovskites for Oxygen Evolution Via Adaptive Machine Learning
AIChE Annual Meeting
Thursday, November 1, 2018 - 2:18pm to 2:36pm
In this regard, we introduce a novel electronic-structure descriptor, namely DOS entropy index, for representing the perovskite in the machine-learning models. The DOS entropy index characterizes the dissimilarities of the perovskitesâ complex electronic structures according to the pairwise Kullback-Leibler (KL) divergence of the associating atomic orbital-wise projected density of states (PDOS). Based on the selection of reference systems, the DOS entropy index has two generalized forms, i.e., atomic pairwise entropy index and entropy eigen-spectrum. Starting from a limited ~20 perovskitesâ DFT-calculated properties and the corresponding OER activities from the literatures, the Bayesian neural network optimized by the DOS entropy index is able to inference above 1000 double perovskitesâ OER activity likelihoods and the associating prediction uncertainties. In practice, our belief towards the model prediction accuracy can be recursively improved by validating the predicted materials with high uncertainties in the actual experiments and incorporate the validation information into the training sets. This adaptive learning framework shows great value in the practical applications for the large-scale exploration of perovskitesâ measured OER activities using the easily accessible computational descriptors. Furthermore, the quantified uncertainties by the Bayesian rules also allow a potential improvement of the modelâs generalizability with a minimum cost of experiment validation efforts.
 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.