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RESEARCH PAPER · Small Methods · 2019

Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine Learning

Shuaihua Lu, Qionghua Zhou, Liang Ma, Yilv Guo, Jinlan Wang

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Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine LearningOriginalAutonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonomous Discovery in the Chemical Sciences Part I: Progress317Activity Origin and Design Principles for Oxygen Reduction on Dual-Metal-Site Catalysts: A Combined Density Functional Theory and Machine Learning Study219Perspective on theoretical methods and modeling relating to electro-catalysis processes95Machine learning: Accelerating materials development for energy storage and conversion370Magnetic two‐dimensional layered crystals meet with ferromagnetic semiconductors109Discovery of Novel Two-Dimensional Photovoltaic Materials Accelerated by Machine Learning51Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique89First‐principles Studies of Band Gap Engineering in Ferroelectric Oxides6Coupling a Crystal Graph Multilayer Descriptor to Active Learning for Rapid Discovery of 2D Ferromagnetic Semiconductors/Half‐Metals/Metals146Machine learning for halide perovskite materials130Critical review of machine learning applications in perovskite solar research139Pressing challenges of halide perovskite thin film growth67Accelerated design of promising mixed lead-free double halide organic–inorganic perovskites for photovoltaics using machine learning44Developing sustainable, high-performance perovskites in photocatalysis: design strategies and applications254Machine Learning Accelerated Insights of Perovskite Materials3Predicting the Electronic and Structural Properties of Two-Dimensional Materials Using Machine Learning14First-principles calculations of electronic, optical and transport properties of the inorganic metal halide perovskite CsBI2Br (B = Sn, Ge, Pb) compounds53Machine learning for perovskite materials design and discovery536Machine learning aided design of perovskite oxide materials for photocatalytic water splitting156Extending Shannon's ionic radii database using machine learning81Machine Learning Boosts the Design and Discovery of Nanomaterials138Rapid discovery of narrow bandgap oxide double perovskites using machine learning59Coupling between interfacial strain and oxygen vacancies at complex-oxides interfaces31Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints31Alternative Lone‐Pair ns 2 ‐Cation‐Based Semiconductors beyond Lead Halide Perovskites for Optoelectronic Applications78Encoding the atomic structure for machine learning in materials science85Prediction of the Curie temperatures of ferroelectric solid solutions using machine learning methods19Studies on the regularity of perovskite formation via machine learning35Is machine learning redefining the perovskite solar cells?59Deep Learning Accelerates the Discovery of Two‐Dimensional Catalysts for Hydrogen Evolution Reaction57Search for ABO3 Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies65Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts32A Genetic Algorithm-Based Artificial Network Method for Material Feature Recombination1A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness335Accelerated Discovery of Single‐Atom Catalysts for Nitrogen Fixation via Machine Learning80On-the-fly interpretable machine learning for rapid discovery of two-dimensional ferromagnets with high Curie temperature92Machine learning accelerated search for new double perovskite oxide photocatalysis7Recent progress in the data-driven discovery of novel photovoltaic materials44Shedding light on the energy applications of emerging 2D hybrid organic-inorganic halide perovskites56A machine learning model for screening thermodynamic stable lead-free halide double perovskites44Electronic and optical properties of CsGeX 2 M (X ,M = Br, Cl, I) perovskites for solar cell applications: First-principles study using PBE and TB-mBJ potentials32Global instability index as a crystallographic stability descriptor of halide and chalcogenide perovskites43Text Mining for Energy Materials3Accelerated discovery of high-performance piezocatalyst in BaTiO3-based ceramics via machine learning84Toward Eco‐Friendly Lead‐Free Low‐Dimensional Perovskites13Perovskite synthesizability using graph neural networks80Discovery of direct band gap perovskites for light harvesting by using machine learning60High-Throughput Synthesis of Thin Films for the Discovery of Energy Materials: A Perspective16Improving Symbolic Regression for Predicting Materials Properties with Iterative Variable Selection62First-principles investigation of lead-free trigonal CsGeI3-xBrx mixed-halide perovskite system for optoelectronic applications: Electronic and optical properties23Applications of machine learning in perovskite materials80Mapping the Room-Temperature Dynamic Stabilities of Inorganic Halide Double Perovskites8Machine Learning‐Assisted Microfluidic Synthesis of Perovskite Quantum Dots34Machine learning-driven design of promising perovskites for photovoltaic applications: A review18Compositional engineering of perovskites with machine learning18Discovery of all-inorganic lead-free perovskites with high photovoltaic performance via ensemble machine learning12Discovery of Novel Photocatalysts Using Machine Learning Approach4Machine learning for CO 2 conversion driven by dielectric barrier discharge plasma and Cs 2 TeCl 6 photocatalysts27Machine learning in energy chemistry: introduction, challenges and perspectives31Descriptor engineering in machine learning regression of electronic structure properties for 2D materials17CoSMoR: Decoding decision-making process along continuous composition pathways in machine learning models trained for material properties3Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87Accelerated search for new ferroelectric materials17Machine-learning-based prediction of cubic perovskite formation energy and magnetism3A Data-Driven Platform for Two-Dimensional Hybrid Lead-Halide Perovskites18Outliers in Shannon’s effective ionic radii table and the table extension by machine learning34Synthesis of carbon dots with predictable photoluminescence by the aid of machine learning33First-principles investigation of stable lead-free halide perovskite materials CsSnCl x Br y I3−x−y for solar cell applications16Performance prediction and optimization of perovskite solar cells based on the Bayesian approach38Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review10Accelerated feasible screening of flame-retardant polymeric composites using data-driven multi-objective optimization13Computational Design of Antiperovskite Solid Electrolytes35Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives76Machine‐Learning Accelerating the Development of Perovskite Photovoltaics12Absorber Layer Thickness as a New Feature in Statistical Learning Tools of Perovskite Solar Cells5Accelerated Discovery of Targeted Environmentally Friendly A(II)B(I)X3-Type Three-Dimensional Hybrid Organic–Inorganic Perovskites for Potential Light Harvesting via Machine Learning5Rapid screening B-site doping ferroelectric perovskite with high curie temperature for electronic applications by a novel IDBO-RF approach4High-entropy alloy screening for halide perovskites17Enhancing interpretability in the exploration of high-energy conversion efficiency in CsSnBr3−xIx configurations using crystal graph convolutional neural networks and adversarial example methods8Understanding Defects in Perovskite Solar Cells through Computation: Current Knowledge and Future Challenge83Determining magnetic properties of high entropy alloys by molar volume difference predicted by machine learning2First-Principles Study on Stability and Photoelectrical Properties of Hybrid Perovskite Systems FA2BBr4–xIx (B = Pb, Sn, Ge; x = 0, 1, 2, 3, 4)4Machine Learning for Photovoltaic Applications0From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning65Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization29A review on machine learning-guided design of energy materials19A density-functional-theory-based study of the lead-free perovskite materials CsGeX3 and CsGeX2X′ (X, X′ = Cl, Br, I) for photovoltaic applications13Machine Learning Approaches in Advancing Perovskite Solar Cells Research16High-throughput screening and machine learning classification of van der Waals dielectrics for 2D nanoelectronics26Machine learning prediction of materials properties from chemical composition: Status and prospects29Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials188AI4Materials: Transforming the landscape of materials science and enigneering25Machine learning strategies for small sample size in materials science41Highly Efficient Screening of Halide Double Perovskite Optoelectronic Materials Based on Machine learning16Multidimensional high-throughput screening for mixed perovskite materials with machine learning3MELRSNet for accelerating the exploration of novel ultrawide bandgap semiconductors0Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning18Machine learning accelerates composition design of lead-free high performance piezoceramics based on potassium sodium niobate5Efficient and Accurate Prediction of Double Perovskite Quasiparticle Band Gaps via Machine Learning and a Descriptor8Machine Learning Assisted Material Discovery: A Small Data Approach31Transforming Computational Nanotechnology: Accelerating Material Discovery, Design, and Property Prediction through Soft Computing Techniques24Active Learning for Accelerated Discovery of Two-Dimensional Magnetic Topological Materials6Atomistic Modeling of Rare Earth Ions in Photonic Materials5Application of Machine Learning in High-throughput Screening of Binary Alloys for the Hydrogenation of Benzene0Artificial Intelligence-Based Applications in Perovskite Photovoltaic Cells7Data-driven and machine learning-assisted research on ferroelectric materials0Lead-Free Perovskites: Crystallographic Classification and Photocatalysis Insights0Structural and cation-ordering control of electronic and multiferroic properties in Ni-doped PbTiO 3 : a first-principles study using PBE and HSE12s functionals0Polarization tuning of ferroelectric photocatalysts: From conventional selection to AI-guided design for efficient CO2-to-C1 conversion5A DFT Study on Tunable Optoelectronic Properties and Stability of (2-CEA)-Based Perovskites for Solar Cells1A Synergistic Strategy for Data‐Constrained Deep Learning in Materials Science1A machine learning framework for screening and band gap prediction of stable cubic halide perovskites for energy applications3Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization0The Critical Role of Tilting Descriptors in Data‐Driven Property Prediction of Orthorhombic Perovskites0

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