Searching for Hidden Perovskite Materials for Photovoltaic Systems by Combining Data Science and First Principle Calculations

Abstract

Undiscovered perovskite materials for applications in capturing solar lights are explored through the implementation of data science. In particular, 15000 perovskite materials data is analyzed where visualization of the data reveals hidden trends and clustering of data. Random forest classification within machine learning is used in order to predict the band gap of perovskite materials where 18 physical descriptors are revealed to determine the band gap. With trained random forest, 9328 perovskite materials with potential for applications in solar cell materials are predicted. The selected Li and Na based perovskite materials within predicted 9328 perovskite materials are evaluated with first principle calculations where 11 undiscovered Li(Na) based perovskite materials fall into the ideal band gap and formation energy ranges for solar cell applications. Thus, the implementation of data science accelerates the discovery of hidden perovskite materials and the approach can be applied to the materials science in general for searching undiscovered materials.

Chalcogenide Materials and Derivatives for Photovoltaic Applications

“…[33] The interesting debate is still open Reproduced with permission. [29] Copyright 2018, American Chemical Society.…”

Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells

“…Over the last 20 years machine learning (ML) approaches have been increasingly used to accelerate material science discovery, [35][36][37][38][39][40][41][42][43][44] applications include screening crystal structure, [45] rapid searching for thermometric materials, [46,47] predicting material properties from their structure, [48] predicting crystal structure, [36] and screening polymers for energy harvesting applications. [49,50] Until recently however neural networks that mimic the learning process of biological neurons have been a relatively unsuccessful class of machine learning algorithm, as they underperformed most other techniques for machine learning and data classification.…”

Machine Learning for Perovskites' Reap-Rest-Recovery Cycle

“…This discussion is followed by suggestions for future experiments that can provide a robust description of the transient optoelectronic behavior across the entire 3R cycle. Due to the extensive number of perovskites suitable for PV (>9,000), 14 and how each chemical composition has distinct stability limits (i.e., performance response when exposed to the intrinsic and extrinsic parameters displayed in Figure 1A), the implementation of supervised and unsupervised ML routines for the experiments highlighted in this section will enable timely feedback about the conditions for optimizing both rest and recovery.…”

“…58 ML is starting to be implemented in perovskite research, with a modest number of very insightful publications. 14,59 To date, all AI-driven approaches described in the literature focus on the screening of potentially stable chemical compositions. Using a statistical learning model, the evaluation of $1,300 double perovskite oxides (AA'BB'O 6 ) has shown that their bandgap is largely determined by the lowest occupied energy levels of the A site and by the electronegativities of the B site elements, respectively.…”