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RESEARCH PAPER · The Journal of Physical Chemistry Letters · 2020

Discovery of Novel Two-Dimensional Photovoltaic Materials Accelerated by Machine Learning

Hao Jin, Huijun Zhang, Jianwei Li, Tao Wang, Langhui Wan, Hong Yu Guo, Yadong Wei

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Discovery of Novel Two-Dimensional Photovoltaic Materials Accelerated by Machine LearningOriginalMachine learning 5d-level centroid shift of Ce3+ inorganic phosphors31An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery94How Machine Learning Accelerates the Development of Quantum Dots? †35Element Code from Pseudopotential as Efficient Descriptors for a Machine Learning Model to Explore Potential Lead-Free Halide Perovskites15Machine learning for advanced energy materials208The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design18Machine Learning Boosts the Design and Discovery of Nanomaterials138On the Technologies of Artificial Intelligence and Machine Learning for 2D Materials8The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning62Catalyze Materials Science with Machine Learning67Predicting the Formability of Hybrid Organic–Inorganic Perovskites via an Interpretable Machine Learning Strategy71Bandgap prediction of two-dimensional materials using machine learning47Applied Machine Learning for Developing Next‐Generation Functional Materials72Study of the Relationship between Synthesis Descriptors and the Type of Zeolite Phase Formed in ZSM‐43 Synthesis by Using Machine Learning6Accurate bandgap predictions of solids assisted by machine learning44Recent progress in the data-driven discovery of novel photovoltaic materials44Understanding, discovery, and synthesis of 2D materials enabled by machine learning171Unveiling the layer-dependent electronic properties in transition-metal dichalcogenide heterostructures assisted by machine learning20Efficiency and Stability Analysis of 2D/3D Perovskite Solar Cells Using Machine Learning30Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction91Data‐Driven Materials Innovation and Applications187Discovery of Two-Dimensional Multinary Component Photocatalysts Accelerated by Machine Learning28Applications of machine learning in perovskite materials80Machine-learning accelerated identification of exfoliable two-dimensional materials22Solar cells based on 2D Janus group-III chalcogenide van der Waals heterostructures36The Application of Novel Functional Materials to Machine Learning0Recent developments in 2D materials for energy harvesting applications43Efficient discovery of room temperature magnetic transition metal monolayers assisted by artificial neural network7Accurate prediction of semiconductor bandgaps based on machine learning and prediction of bandgaps for two-dimensional heterojunctions14Interpretable machine learning assisted multi-objective optimization design for small molecule hole transport materials14Quantum Machine Learning in Materials Prediction: A Case Study on ABO3 Perovskite Structures30Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review10Phyllosilicates as earth-abundant layered materials for electronics and optoelectronics: Prospects and challenges in their ultrathin limit24From prediction to design: Recent advances in machine learning for the study of 2D materials90Machine learning for carbon dot synthesis and applications50When Machine Learning Meets 2D Materials: A Review156Enhancing interpretability in the exploration of high-energy conversion efficiency in CsSnBr3−xIx configurations using crystal graph convolutional neural networks and adversarial example methods8ML-Aided Computational Screening of 2D Materials for Photocatalytic Water Splitting34Construction and application of materials database under data-driven research paradigm3Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures22Artificial intelligence-driven revolution in nanozyme design: from serendipity to rational engineering59Integrating artificial intelligence in energy transition: A comprehensive review248Application of Machine Learning in Nano Research — A Review2Machine learning-enabled optoelectronic material discovery: a comprehensive review31Explainable Artificial Intelligence Driven Methodology for Accelerated Research of Complex Systems: Case Study of Thin‐Film Photovoltaic Kesterite‐Based Technology7Distributed chemical sensing: An unexplored frontier in urban, industrial, and environmental monitoring1Explainable artificial intelligence relates perovskite luminescence images to current-voltage metrics5Designing AI-powered user-centric platforms for energy consumption and carbon footprint reduction3Machine learning for renewable energy advancements: Prospects and emerging techniques12Exploring the Pareto front of strength-conductivity trade-off: interpretable machine learning for property prediction and composition design in high-strength high-conductivity Cu alloys1Construction of Ag-Sb2Se2Te/SnSe2 heterojunction for self-powered photoelectrochemical photodetector1

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