Data‐driven epoch, the development of machine learning (ML) in materials and device design is an irreversible trend. Its ability and efficiency to handle nonlinear and game‐playing problems is unmatched by traditional simulation computing software and trial‐error experiments. Perovskite solar cells are complex physicochemical devices (systems) that consist of perovskite materials, transport layer materials, and electrodes. Predicting the physicochemical properties and screening the component materials related to perovskite solar cells is the strong point of ML. However, the applications of ML in perovskite solar cells and component materials has only begun to boom in the last two years, so it is necessary to provide a review of the involved ML technologies, the application status, the facing urgent challenges and the development blueprint.

Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

First principle investigation of electronic, optical and thermoelectric features of X2ScAgCl6 (X=K, Na) double perovskites

Within a short period of time, perovskite solar cells (PSC) have attracted paramount research interests among photovoltaic (PV) community. Recently, machine learning (ML) has emerged as a powerful tool for the exploration and rational design of novel materials and similar trends are observed for PSC development. Application of ML in PSC research is significantly accelerating the PSC development as ML methods are being applied to discover new stable perovskite material, optimize device structure and its processing, and in the analyses of the PSC characterization results. In this review, we have summarised an overview of the applications of ML in PSC developments by analyzing the existing literature. We first introduced the workflow of the ML and described each step. Thereafter, the basics of the perovskite materials and the working of PSCs are described in brief. Then, the application of ML in various aspects of PSC development has been extensively discussed which ranges from the prediction of the optoelectronic properties of the perovskite materials, the discovery of new perovskite materials, optimization of PSC device structures and its processing, and the device level analyses of the PSCs. We have summarized the challenges of PSC commercialization and how PSC research could be benefitted by implementation of ML strategies. In conclusion, the current limitations of employing ML in PSC research and their possible solutions are explained. We have also provided potential future research directions of application of ML techniques in PSC research to develop highly efficient and stable PSCs at the end of this review.This article is protected by copyright. All rights reserved.

Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives

Zero-carbon energy and negative emission technologies are crucial for achieving a carbon neutral future, and nanomaterials have played critical roles in advancing such technologies. More recently, due to the explosive growth in data, the adoption and exploitation of artificial intelligence (AI) as part of the materials research framework have had a tremendous impact on the development of nanomaterials. AI has enabled revolutionary next-generation paradigms to significantly accelerate all stages of material discovery and facilitate the exploration of the enormous design space. In this review, we summarize recent advancements of AI applications in nanomaterials discovery, with a special emphasis on the selected applications of AI and nanotechnology for the net-zero emission future including the development of solar cells, hydrogen energy, battery materials for renewable energy, and CO2 capture and conversion materials for carbon capture, utilization and storage (CCUS) technologies. In addition, we discuss the limitations and challenges of current AI applications in this area by identifying the gaps that exist in current development. Finally, we present the prospect for future research directions in order to facilitate the large-scale applications of artificial intelligence for advancements in nanomaterials.

AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS).

Abstract Utilizing artificial intelligent based algorithms in solving engineering problems is widely spread nowadays. Herein, this study provides a comprehensive and insightful analysis of the application of machine learning (ML) models to complex datasets in the field of solar cell power conversion efficiency (PCE). Mainly, perovskite solar cells generate three datasets, varying dataset size and complexity. Various popular regression models and hyperparameter tuning techniques are studied to guide researchers and practitioners looking to leverage machine learning methods for their data-driven projects. Specifically, four ML models were investigated; random forest (RF), gradient boosting (GBR), K-nearest neighbors (KNN), and linear regression (LR), while monitoring the ML model accuracy, complexity, computational cost, and time as evaluating parameters. Inputs' importance and contribution were examined for the three datasets, recording a dominating effect for the electron transport layer's (ETL) doping as the main controlling parameter in tuning the cell's overall PCE. For the first dataset, ETL doping recorded 93.6%, as the main contributor to the cell PCE, reducing to 79.0% in the third dataset. 

Selecting an appropriate machine-learning model for perovskite solar cell datasets

Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cells

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Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cells