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RESEARCH PAPER · Nature Communications · 2018

Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning

Prasanna V. Balachandran, Benjamin A. Kowalski, Alp Sehirlioglu, Turab Lookman

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Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learningOriginalHow To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics316Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning722Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry270Chemical Reaction Spectrum0Chemical Reaction Spectrum0Not Just Par for the Course: 73 Quaternary Germanides RE4M2XGe4 (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for Intermetallics with Low Thermal Conductivity19Machine-Learning-Based Cyclic Voltammetry Behavior Model for Supercapacitance of Co-Doped Ceria/rGO Nanocomposite42The Search for BaTiO3-Based Piezoelectrics With Large Piezoelectric Coefficient Using Machine Learning39High critical transition temperature of lead-based perovskite ferroelectric crystals: A machine learning study12Ternary and Quaternary Rare-Earth Transition-Metal Germanides0Data-Driven Studies of Li-Ion-Battery Materials57Materials informatics: From the atomic-level to the continuum189Machine learning electron correlation in a disordered medium23Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design828Prediction of Synthesis of 2D Metal Carbides and Nitrides (MXenes) and Their Precursors with Positive and Unlabeled Machine Learning346Rocketsled: a software library for optimizing high-throughput computational searches19Machine learning guided design of functional materials with targeted properties60Deep Learning Accelerated Gold Nanocluster Synthesis80Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine Learning116Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures15Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities29Recent advances and applications of machine learning in solid-state materials science2604Global discovery of stable and non-toxic hybrid organic-inorganic perovskites for photovoltaic systems by combining machine learning method with first principle calculations99Accelerated Search for BaTiO3‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design100Preparative History vs Driving Force in Water Oxidation Catalysis: Parameter Space Studies of Cobalt Spinels13Prediction and optimization of epoxy adhesive strength from a small dataset through active learning102Virtual Issue on Machine-Learning Discoveries in Materials Science29Accelerated discovery of potential ferroelectric perovskiteviaactive learning35Bayesian Global Optimization applied to the design of shape-memory alloys6Bayesian Optimization in Materials Science6Bayesian-optimization-guided experimental search of NASICON-type solid electrolytes for all-solid-state Li-ion batteries72Learning with Large Databases0Learning with Small Databases0Navigating the design space of inorganic materials synthesis using statistical methods and machine learning43A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation131Harnessing Legacy Data to Educate Data-Enabled Structural Materials Engineers4Discovering new perovskites with artificial intelligence24Artificial Intelligence to Power the Future of Materials Science and Engineering153Closed-loop optimization of fast-charging protocols for batteries with machine learning1146Machine learning: Accelerating materials development for energy storage and conversion370Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods10Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique89Machine Learning in Materials Discovery: Confirmed 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descriptors23High-dimensional Bayesian optimization of 23 hyperparameters over 100 iterations for an attention-based network to predict materials property: A case study on CrabNet using Ax platform and SAASBO33Theory-guided design of high-strength, high-melting point, ductile, low-density, single-phase BCC high entropy alloys67Ferroelectric N-polar ScAlN/GaN heterostructures grown by molecular beam epitaxy69Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning102Improved description of the potential energy surface in BaTiO3 by anharmonic phonon coupling21

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