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RESEARCH PAPER · Nature Reviews Materials · 2016

Computational predictions of energy materials using density functional theory

Anubhav Jain, Yongwoo Shin, Kristin Aslaug Persson

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Computational predictions of energy materials using density functional theoryOriginalResearch Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases153Materials science with large-scale data and informatics: Unlocking new opportunities237Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties216Can artificial intelligence create the next wonder material?125Computational Approach for Epitaxial Polymorph Stabilization through Substrate Selection129Rational Co‐Design of Polymer Dielectrics for Energy Storage189Methods and Protocols for Electrochemical Energy Storage Materials Research175Progress in discovery and structural design of color conversion phosphors for LEDs1095A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds242Perovskite Solar Cells: Progress and Advancements178Toward Lead-Free Perovskite Solar Cells1139Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning141Evaluating structure selection in the hydrothermal growth of FeS2 pyrite and marcasite86Multi-fidelity machine learning models for accurate bandgap predictions of solids343SnO as a potential oxide thermoelectric candidate89Capturing Anharmonicity in a Lattice Thermal Conductivity Model for High-Throughput Predictions108Uncovering structure-property relationships of materials by subgroup discovery120Phosphate Framework Electrode Materials for Sodium Ion Batteries379Stratified construction of neural network based interatomic models for multicomponent materials84Thermodynamics of Phase Selection in MnO2 Framework Structures through Alkali Intercalation and Hydration231Rational Design: A High-Throughput Computational Screening and Experimental Validation Methodology for Lead-Free and Emergent Hybrid Perovskites246Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment192Novel Methods for Sodium‐Ion Battery Materials113Benchmarking Quantum Chemical Methods: Are We Heading in the Right Direction?181Two-dimensional MXenes for energy storage and conversion applications177An ab initio electronic transport database for inorganic materials233Mechanisms of Lithium Intercalation and Conversion Processes in Organic–Inorganic Halide Perovskites158Thermodynamic Routes to Novel Metastable Nitrogen-Rich Nitrides173High-Performance Pyrochlore-Type Yttrium Ruthenate Electrocatalyst for Oxygen Evolution Reaction in Acidic Media499Electronic Structure Calculations of Hydrogen Storage in Lithium-Decorated Metal–Graphyne Framework80Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows358Influence of Inversion on Mg Mobility and Electrochemistry in Spinels90Materials and Systems for Organic Redox Flow Batteries: Status and Challenges464A universal strategy for the creation of machine learning-based atomistic force fields230The curious case of two dimensional Si2BN: A high-capacity battery anode material163Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning471Nanocatalysts for hydrogen evolution reactions144High Energy Density Dielectrics Based on PVDF‐Based Polymers76Engineered Transport in Microporous Materials and Membranes for Clean Energy Technologies126Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds1850Towards predictive many-body calculations of phonon-limited carrier mobilities in semiconductors332Inverse design in search of materials with target functionalities513Dissolution of Complex Metal Oxides from First-Principles and Thermodynamics: Cation Removal from the (001) Surface of Li(Ni1/3Mn1/3Co1/3)O282Accelerating the discovery of materials for clean energy in the era of smart automation776Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons201Design and exploration of semiconductors from first principles: A review of recent advances183Progress and prospects of next-generation redox flow batteries335Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction113Understanding crystallization pathways leading to manganese oxide polymorph formation164Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions513Machine Learning Directed Search for Ultraincompressible, Superhard Materials317The 2019 materials by design roadmap322Understanding migration barriers for monovalent ion insertion in transition metal oxide and phosphate based cathode materials: A DFT study89Computation-Accelerated Design of Materials and Interfaces for All-Solid-State Lithium-Ion Batteries398Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry270Active Sites in 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