Machine learning for perovskite materials design and discovery

Abstract

The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.

Investigation of Opto-Electronic Properties and Stability of Mixed-Cation Mixed-Halide Perovskite Materials with Machine-Learning Implementation

“…Several ML techniques were analyzed for the prediction of opto-electronic properties as detailed by Workman et al [29]. ML methods and strategies to select potential candidates for opto-electronic applications are presented in a recent review [30].…”

A Machine Learning Approach for Metal Oxide Based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells

“…and designing of innovative perovskite for the development of PSCs. [29] Our aim is to put forward a direct appraisal between experimental data and machine learning algorithms for PSCs. Radio frequency (rf) plasma-enhanced method was used to synthesize WO 3 -conjugated polymer composites as charge selective layer.…”

“…Subsequently, machine learning approach has been applied to predict the properties of perovskite (such as phase stability, band gap, electronic transport features, etc.) and designing of innovative perovskite for the development of PSCs [29] . Our aim is to put forward a direct appraisal between experimental data and machine learning algorithms for PSCs.…”

Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

“…In recent years, ABO 3 perovskite composite oxides have attracted great interest [3,4,[7][8][9][10][11][12][13][14][15]. Research has focused on the development of new perovskite materials to improve activity, selectivity, and stability, as well as the development of advanced manufacturing techniques to reduce their cost while ensuring their reliability, safety, and reproducibility [14][15][16]. In ABO 3 perovskite oxides, the A site is the rare earth or alkaline earth metal ions, which usually stabilize the structure, while the B site is occupied by the smaller transition metal ions [17].…”

“…In ABO 3 perovskite oxides, the A site is the rare earth or alkaline earth metal ions, which usually stabilize the structure, while the B site is occupied by the smaller transition metal ions [17]. Through partial substitution of A and B sites, multi-component perovskite compounds can be combined [16]. When A or B sites are partially replaced by other metal ions, anion defects or B sites at different valences can be formed.…”