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RESEARCH PAPER · Frontiers in Materials · 2016

Finding New Perovskite Halides via Machine Learning

Ghanshyam Pilania, Prasanna V. Balachandran, Chiho Kim, Turab Lookman

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Finding New Perovskite Halides via Machine LearningOriginalRational Design: A High-Throughput Computational Screening and Experimental Validation Methodology for Lead-Free and Emergent Hybrid Perovskites246Extracting knowledge from molecular mechanics simulations of grain boundaries using machine learning54Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations412Machine Learning and Materials Informatics: Recent Applications and Prospects8Mining Materials Design Rules from Data: The Example of Polymer Dielectrics62Virtual screening of inorganic materials synthesis parameters with deep learning189Machine learning in materials informatics: recent applications and prospects1914Identifying models of dielectric breakdown strength from high-throughput data via genetic programming30Machine learning properties of binary wurtzite superlattices31Electronic structure, magnetic, mechanical and thermo-physical behavior of double perovskite Ba2MgOsO658High pressure and high temperature investigation of metallic perovskite SnTaO310The geometric blueprint of perovskites260Density Functional Theory – Machine Learning Approach to Analyze the Bandgap of Elemental Halide Perovskites and Ruddlesden‐Popper Phases36Toward Eco-friendly and Stable Perovskite Materials for Photovoltaics330Predicting the electronic structure, magnetism, and transport properties of new Co-based Heusler alloys52Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies134Halogen in materials design: Chloroammonium lead triiodide perovskite (ClNH3PbI3) a dynamical bandgap semiconductor in 3D for photovoltaics4A combined DFT, DFT + U and mBJ investigation on electronic structure, magnetic, mechanical and thermodynamics of double perovskite Ba2ZnOsO659Prediction of superconducting transition temperature using a machine-learning method10Ab-initio DFT based investigation of double perovskite oxide Ba2CdOsO6 with cubic structure60Rationalizing Perovskite Data for Machine Learning and Materials Design101Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds116Predicting electron-phonon coupling constants of superconducting elements by machine learning16Bismuth halide semiconductors : a combined theoretical and experimental approach for photovoltaics0Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases104Computational Modeling and the Design of Perovskite Solar Cells3Rationalizing the interphase stability of Li|doped-Li 7 La 3 Zr 2 O 12 via automated reaction screening and machine learning97White light emission in low-dimensional perovskites217Thermodynamic Stability Landscape of Halide Double Perovskites via High‐Throughput Computing and Machine Learning232New tolerance factor to predict the stability of perovskite oxides and halides1838From DFT to machine learning: recent approaches to materials science–a review849Machine learning based prediction of metal hydrides for hydrogen storage, part I: Prediction of hydrogen weight percent135Machine learning based prediction of metal hydrides for hydrogen storage, part II: Prediction of material class84Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators13Data mining new energy materials from structure databases57Data analysis of multi-dimensional thermophysical properties of liquid substances based on clustering approach of machine learning13Materials Design in Digital Era: Challenges and Opportunities4KBaTeBiO6: A Lead-Free, Inorganic Double-Perovskite Semiconductor for Photovoltaic Applications64Alternative materials for perovskite solar cells from materials informatics29Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics185Learn-and-Match Molecular Cations for Perovskites50Prediction of new iodine-containing apatites using machine learning and density functional theory17Tin Halide Perovskites: Progress and Challenges229Osmium Containing Double Perovskite Ba2XOsO6 (X = Mg, Zn, Cd): Important Candidates for Half-Metallic Ferromagnetic and Spintronic Applications2A Statistical Learning Framework for Accelerated Bandgap Prediction of Inorganic Compounds7Exploiting Ionic Radii for Rational Design of Halide Perovskites128Computational Modeling and the Design of Perovskite Solar Cells0Materials Representations1Searching for stable perovskite solar cell materials using materials genome techniques and high-throughput calculations40A Critical Review of Machine Learning of Energy Materials606Low‐dimensional metal halide perovskites and related optoelectronic applications121Discovering new perovskites with artificial intelligence24Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods10Hot Carriers in Halide Perovskites: How Hot Truly?73An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts135High-Throughput Computational Studies in Catalysis and Materials Research, and Their Impact on Rational Design5Broad-band emission in metal halide perovskites: Mechanism, materials, and applications288Experimental, computational and thermodynamic studies in perovskites metal oxides for thermochemical fuel production: A review94Machine Learning for Predicting the Band Gaps of ABX 3 Perovskites from Elemental Properties182Polymorphous nature of cubic halide perovskites203Machine learning in materials genome initiative: A review231Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery281Accelerating inverse crystal structure prediction by machine learning: A case study of carbon allotropes26Search for oxide glass compositions using Bayesian optimization with elemental-property-based descriptors9Two-dimensional hybrid organic–inorganic perovskites as emergent ferroelectric materials48First-principles investigation of the physical properties of indium based fluoroperovskites InAF3 (A = Ca, Cd and Hg)34Machine learning for halide perovskite materials130Explainable machine learning for materials discovery: predicting the potentially formable Nd–Fe–B crystal structures and extracting the structure–stability relationship14Machine learning (ML)‐assisted optimization doping of KI in MAPbI 3 solar cells40Critical review of machine learning applications in perovskite solar research139Integrated Experimental and Theoretical Approach for Efficient Design and Synthesis of Gold-Based Double Halide Perovskites33Small Data Machine Learning: Classification and Prediction of Poly(ethylene terephthalate) Stabilizers Using Molecular Descriptors32Crystal structure classification in ABO3 perovskites via machine learning73Center-Environment Feature Model for Machine Learning Study of Spinel Oxides Based on First-Principles Computations40CINEMAS: Comprehensively INtegrated Environment for advanced MAterials Simulations3Anti-perovskites for solid-state batteries: recent developments, current challenges and future prospects137Developing sustainable, high-performance perovskites in photocatalysis: design strategies and applications254Machine Learning Accelerated Insights of Perovskite Materials3Perovskite materials as photocatalysts: Current status and future perspectives28Data-driven enhancement of cubic phase stability in mixed-cation perovskites30The 2D Halide Perovskite Rulebook: How the Spacer Influences Everything from the Structure to Optoelectronic Device Efficiency1085Machine learning for advanced energy materials208Machine learning for perovskite materials design and discovery536Multi-objective Bayesian optimization of optical glass compositions15Machine learning in materials science: From explainable predictions to autonomous design281Beyond Tolerance Factor: Using Deep Learning for Prediction Formability of ABX3 Perovskite Structures8Graph representational learning for bandgap prediction in varied perovskite crystals35Comparative analysis of machine learning approaches on the prediction of the electronic properties of perovskites: A case study of ABX3 and A2BB’X635Structure and Dynamics of Energy Materials from Machine Learning Simulations: A Topical Review†12Machine learning elastic constants of multi-component alloys68Studies on the regularity of perovskite formation via machine learning35Is machine learning redefining the perovskite solar cells?59Predicting the Formability of Hybrid Organic–Inorganic Perovskites via an Interpretable Machine Learning Strategy71Crystal-Site-Based Artificial Neural Networks for Material Classification5Search methods for inorganic materials crystal structure prediction27Application of Bayesian Optimization and Regression Analysis to Ferromagnetic Materials Development19Molecular Bond Engineering and Feature Learning for the Design of Hybrid Organic–Inorganic Perovskite Solar Cells with Strong Noncovalent Halogen–Cation Interactions6Electronic structure and optical response of double perovskite Rb2NaCoF6 for optoelectronic devices59Recent progress in the data-driven discovery of novel photovoltaic materials44Robust stability, half metallic Ferromagnetism and structural properties of Co2RhSi, and Co2RuSi Heusler compounds-A first principles approach22A machine learning model for screening thermodynamic stable lead-free halide double perovskites44ABO3 Perovskites’ Formability Prediction and Crystal Structure Classification using Machine Learning9Machine learning approaches for prediction of properties of natural fiber composites : Apriori algorithm18Electronic and optical properties of cubic bulk and ultrathin surface [001] slab of CsPbBr 312Perovskite synthesizability using graph neural networks80The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning10Predicting the formation of fractionally doped perovskite oxides by a function-confined machine learning method39Effects of data bias on machine-learning–based material discovery using experimental property data24Structural, elastic, electronic, optical and thermoelectric response of lead-free double perovskite Rb2TlInX6 (X=Cl, I) for energy storage devices: DFT+SOC investigations74Feature Fusion Deep Learning Model for Defects Prediction in Crystal Structures4Topological feature engineering for machine learning based halide perovskite materials design71Accelerated innovation in developing high-performance metal halide perovskite solar cell using machine learning19Mapping the Room-Temperature Dynamic Stabilities of Inorganic Halide Double Perovskites8Prediction of hydrogen storage in dibenzyltoluene empowered with machine learning38Machine learned synthesizability predictions aided by density functional theory30Hydrogen Storage Prediction in Dibenzyltoluene as Liquid Organic Hydrogen Carrier Empowered with Weighted Federated Machine Learning26Insight into the electronic structure, magnetic, and thermoelectric properties of transition metal pnictides KCr2L2 (K= Ca, Sr; L = P, As): As substitute source for renewing energy20A comprehensive DFT study to evaluate the modulation in the band gap, elastic, and optical behaviour of CsPbBr3 under the effect of stress31Advances in Synthesis and Defect Properties of Halide Perovskite Nanocrystals: Experimental and Theoretical Perspectives1First-principles calculations to investigate physical properties of orthorhombic perovskite YBO 3 (B = Ti & Fe) for high energy applications23Geometric and topological AI for molecular sciences0Machine learning in energy chemistry: introduction, challenges and perspectives31Solar Cells and Relevant Machine Learning3Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data27Exploring the Evolution of Metal Halide Perovskites via Latent Representations of the Photoluminescent Spectra6Enhancing property prediction and process optimization in building materials through machine learning: A review161Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170Caesium manganese fluoride cubic-perovskite nanoparticles – synthesis, luminescence and magnetic properties2Accelerated prediction of perovskite material properties with classical machine learning and graph neural network6Machine learning for perovskite solar cell design58Perspective on machine learning based investigated materials to realize Stable-Efficient-Toxin free (S-E-T) perovskite solar cells7Accelerated design of lead-free high-performance piezoelectric ceramics with high accuracy via machine learning33DFT-based computational investigations of structural, mechanical, optoelectronics, and thermoelectric properties of InXF3 (X = Be and Sr) ternary fluoroperovskites compounds46AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)122A state-of-the-art review on the utilization of machine learning in nanofluids, solar energy generation, and the prognosis of solar power44SLI-GNN: A Self-Learning-Input Graph Neural Network for Predicting Crystal and Molecular Properties30The spin-polarized electronic and optical properties of novel Ba2TaXO6 (X = Co, Fe, In) for optoelectronic applications: An ab-initio study16First-principles investigations of the mechanically and thermodynamically stable potassium-based double perovskites K2TlAsX6 (X = Cl, Br) for optoelectronic and renewable applications93Computational Design of Antiperovskite Solid Electrolytes35Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives76An evolutionary variational autoencoder for perovskite discovery11Machine‐Learning Accelerating the Development of Perovskite Photovoltaics12A systemic study on Thallium based 3D halide perovskite with enhanced figure of merit5Absorber Layer Thickness as a New Feature in Statistical Learning Tools of Perovskite Solar Cells5Accelerating the prediction of stable materials with machine learning104Prediction of hydrogen generation from perhydro-dibenzyltoluene empowered with machine learning6Scope of machine learning in materials research—A review230CuPBX 3 and AgPBX 3 Inorganic Perovskites for Solar Cell Applications0High-entropy alloy screening for halide perovskites17Machine learning approaches for screening of materials in flexible electronic devices0

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