Multiobjective Stepwise Design Strategy-Assisted Design of High-Performance Perovskite Oxide Photocatalysts

Abstract

The rapid discovery of high-performance photocatalysts is one of the challenges in the field of photocatalysis research. Here, a multiobjective stepwise design strategy is developed to accelerate the design of potential ABO 3type perovskites with enhanced photocatalytic activity. The strategy includes model building, stepwise screening, and prediction of the hydrogen production rate (R H 2 ) of candidate perovskites. Based on the strategy, 35 candidate perovskites with a high specific surface area (SSA) (>60 m 2 g −1 ), a suitable bandgap (E g ) (1.4−2.6 eV), and a small crystallite size (CS) (<36 nm) are proposed. The predicted results of R H 2 of the candidates show they have high R H 2 (>6000 μmol h −1 g −1 ). The statistical analysis results reveal that a suitable E g is more likely to acquire when the main elements at the A site are Bi, La, and Pr, and those at the B site are Fe, Ti, and Mn. The perovskites prepared using the sol−gel, polymer complex, combustion method, and lower calcination temperature preferably have a higher SSA and a smaller CS. The relationship between target properties and R H 2 demonstrates that candidate perovskites with E g in the range of 1.9−2.4 eV, CS in the range of 20−32 nm, and a high SSA (>50 m 2 g −1 ) have a higher R H 2 . Furthermore, these models have been developed for online prediction applications.

Small data machine learning in materials science

“…As shown in Fig. 2, the workflow of materials machine learning includes data collection, feature engineering, model selection and evaluation, and model application [20][21][22][23] .…”

A machine learning-based nano-photocatalyst module for accelerating the design of Bi2WO6/MIL-53(Al) nanocomposites with enhanced photocatalytic activity

“…The ML calculations of our work were performed using the HyperMiner soware package 59,60 and the Online Computational Platform of Material Data Mining (OCPMDM), 61,62 which we developed. HyperMiner can be freely downloaded on the website: http://materials-data-mining.com/home.…”

Machine Learning‐Assisted Discovery of 2D Perovskites with Tailored Bandgap for Solar Cells

“…[ 1 ] Thus, we can save a lot of time and resources by screening functional materials through machine learning (ML) models. [ 2–6 ]…”

“…[1] Thus, we can save a lot of time and resources by screening functional materials through machine learning (ML) models. [2][3][4][5][6] Recently, organic-inorganic halide perovskite solar cells (PSCs) have attracted much attention because of their low cost, light weight, and simple manufacture, which meet the needs of future development. [7,8] However, the instability of materials and their sensitivity to environmental factors such as water, heat, oxygen, and UV light have become the most pressing issue restricting their further development.…”