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RESEARCH PAPER · Chem · 2016

Computational Screening of All Stoichiometric Inorganic Materials

Daniel W. Davies, Keith T. Butler, Adam Jackson, Andrew J. Morris, Jarvist Moore Frost, Jonathan Michael Skelton, Aron Walsh

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Computational Screening of All Stoichiometric Inorganic MaterialsOriginalMaterial informatics driven design and experimental validation of lead titanate as an aqueous solar photocathode27Application of computational methods to the design and characterisation of porous molecular materials78Designing porous electronic thin-film devices: band offsets and heteroepitaxy49Electronic structure design for nanoporous, electrically conductive zeolitic imidazolate frameworks67Energy level alignment at semiconductor–water interfaces from atomistic and continuum solvation models18Electroactive Nanoporous Metal Oxides and Chalcogenides by Chemical Design11Grand Challenges and Future Opportunities for Metal–Organic Frameworks434Perspective: Theory and simulation of hybrid halide perovskites140Perspective: Theory and simulation of hybrid halide perovskites86Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning202Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure70Materials discovery by chemical analogy: role of oxidation states in structure prediction36The chemical forces underlying octahedral tilting in halide perovskites38Recombinant Escherichia coli as a biofactory for various single- and multi-element nanomaterials135Design and exploration of semiconductors from first principles: A review of recent advances183AFLOW-ML: A RESTful API for machine-learning predictions of materials properties112Machine learning for molecular and materials science4884Benchmarking Chemical Stability of Arbitrarily Mixed 3D Hybrid Halide Perovskites for Solar Cell Applications30Computational Design of Photovoltaic Materials4Meta-MOFs: Framework Materials with Anomalous Behavior64Candidate Inorganic Photovoltaic Materials from Electronic Structure-Based Optical Absorption and Charge Transport Proxies77Rocketsled: a software library for optimizing high-throughput computational searches19Data mining new energy materials from structure databases57Modelling of framework materials at multiple scales: current practices and open questions24SMACT: Semiconducting Materials by Analogy and Chemical Theory67Data-Driven Discovery of Photoactive Quaternary Oxides Using First-Principles Machine Learning49Chalcogenide Materials and Derivatives for Photovoltaic Applications107Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells61Descriptors for Electron and Hole Charge Carriers in Metal Oxides31Examination of high-throughput hybrid calculations using coarser reciprocal space meshes10Ba 6 In 6 Zn 4 Se 19 : a high performance infrared nonlinear optical crystal with [InSe 3 ] 3− trigonal planar functional motifs22Bayesian Optimization in Materials Science6Machine-learning-assisted search for functional materials over extended chemical space25Optically pumped white light-emitting diodes based on metal halide perovskites and perovskite-related materials46Toward Accelerated Thermoelectric Materials and Process Discovery102Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions70Computational functionality‐driven design of semiconductors for optoelectronic applications58Chiral-perovskite optoelectronics908Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals49Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials294Data-Mining Element Charges in Inorganic Materials0Data-Mining Element Charges in Inorganic Materials5Biosynthesis of inorganic nanomaterials using microbial cells and bacteriophages181Learning Representations of Inorganic Materials from Generative Adversarial Networks15A critical examination of compound stability predictions from machine-learned formation energies194Perovskite-inspired materials for photovoltaics and beyond—from design to devices206Predicting materials properties without crystal structure: deep representation learning from stoichiometry400An Invertible Crystallographic Representation for General Inverse Design of Inorganic Crystals with Targeted Properties1Generative models for inverse design of inorganic solid materials38Microbial biomanufacture of metal/metallic nanomaterials and metabolic engineering: design strategies, fundamental mechanisms, and future opportunities39Thermodynamic exploration of xenon/krypton separation based on a high-throughput screening16Interpretable discovery of new semiconductors with machine learning5Identification of Potential Optoelectronic Applications for Metal Thiophosphates19Predicting stable crystalline compounds using chemical similarity113Discovery of a Low Thermal Conductivity Oxide Guided by Probe Structure Prediction and Machine Learning29Discovery of a Low Thermal Conductivity Oxide Guided by Probe Structure Prediction and Machine Learning2The Design of Perovskites for Light-Emitting Applications02021 roadmap for sodium-ion batteries273Machine Learning in Materials Science24Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts32Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry80Two low-expansion Li-ion cathode materials with promising multi-property performance3Predicting energy and stability of known and hypothetical crystals using graph neural network43Accelerating materials discovery with Bayesian optimization and graph deep learning142Perspectives for next generation lithium-ion battery cathode materials102Synthetic accessibility and stability rules of NASICONs121Entropy-based active learning of graph neural network surrogate models for materials properties35Anharmonic lattice dynamics of superionic lithium nitride29Targeted design of advanced electrocatalysts by machine learning110TCSP: a Template based crystal structure prediction algorithm and web server for materials discovery2An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties189Towards Predictive Synthesis of Inorganic Materials Using Network Science5Molecular representations for machine learning applications in chemistry94High-throughput computational screening of nanoporous materials in targeted applications65Ionic liquids and deep eutectics as a transformative platform for the synthesis of nanomaterials106Predicting compositional changes of organic–inorganic hybrid materials with Augmented CycleGAN4Highly accurate machine learning prediction of crystal point groups for ternary materials from chemical formula19Heterogeneous Catalysis in Grammar School8An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties18Environmental Stability of Crystals: A Greedy Screening17Distributed representations of atoms and materials for machine learning41Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation118MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art70TCSP: a Template-Based Crystal Structure Prediction Algorithm for Materials Discovery32Perovskite synthesizability using graph neural networks80Additive single atom values for thermodynamics II: Enthalpies, entropies and Gibbs energies for formation of ionic solids10Machine Learning Approaches for Accelerating the Discovery of Thermoelectric Materials15The physical significance of imaginary phonon modes in crystals176Resampling Techniques for Materials Informatics: Limitations in Crystal Point Groups Classification11Rapid discovery of stable materials by coordinate-free coarse graining62Design of New Ternary Nitrides for Photovoltaic Applications via High-Throughput Calculations8Anaerobiosis favors biosynthesis of single and multi-element nanostructures5Phase Stability Through Machine Learning23Emerging Chalcohalide Materials for Energy Applications163Accelerated chemical space search using a quantum-inspired cluster expansion approach25A computationally-guided non-equilibrium synthesis approach to materials discovery in the SrO–Al 2 O 3 –SiO 2 phase field6Element similarity in high-dimensional materials representations15Earth-abundant photoelectrodes for water splitting and alternate oxidation reactions: Recent advances and future perspectives69Benzoquinoline Chemical Space: A Helpful Approach in Antibacterial and Anticancer Drug Design1Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics137Predicting Synthesizability using Machine Learning on Databases of Existing Inorganic Materials34Target‐Driven Design of Deep‐UV Nonlinear Optical Materials via Interpretable Machine Learning125A critical examination of robustness and generalizability of machine learning prediction of materials properties144Symmetry-based computational search for novel binary and ternary 2D materials33Designing materials acceleration platforms for heterogeneous CO2 photo(thermal)catalysis28A Two-Step Machine Learning Method for Predicting the Formation Energy of Ternary Compounds7The role of machine learning in perovskite solar cell research42Exploring battery cathode materials in the Li-Ni-O phase diagrams using structure prediction11Deep Generative Model for Inverse Design of High-Temperature Superconductor Compositions with Predicted Tc > 77 K23Closed‐Loop Error‐Correction Learning Accelerates Experimental Discovery of Thermoelectric Materials14Interpretable discovery of semiconductors with machine learning45Accelerating Materials Discovery through Machine Learning: Predicting Crystallographic Symmetry Groups16Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions38Design of New Inorganic Crystals with the Desired Composition Using Deep Learning9In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99ScGAN: a generative adversarial network to predict hypothetical superconductors5Drawing a materials map with an autoencoder for lithium ionic conductors6A prediction-driven database to enable rapid discovery of nonlinear optical materials46Dynamic sampling of liquid metal structures for theoretical studies on catalysis20Google AI and robots join forces to build new materials7Chalcogenide Perovskite, An Emerging Photovoltaic Material: Current Status and Future Perspectives35AI-driven inorganic materials by design0Dismai-Bench: benchmarking and designing generative models using disordered materials and interfaces5From computational screening to the synthesis of a promising OER catalyst28Future prospects and grand challenges for porous coordination polymers0Inverse design of semiconductor materials with deep generative models26High‐performance diffusion model for inverse design of high Tc superconductors with effective doping and accurate stoichiometry18Inferring energy–composition relationships with Bayesian optimization enhances exploration of inorganic materials6Accelerated Data-Driven Discovery and Screening of Two-Dimensional Magnets Using Graph Neural Networks21Spatially resolved lock-in micro-thermography (SR-LIT): A tensor analysis-enhanced method for anisotropic thermal characterization14Mapping inorganic crystal chemical space28Effect of Changing and Combining Trivalent Metals in the Structural and Electronic Properties of Cu-Based Crystal Delafossite Materials3Diffusion models for conditional generation of hypothetical new families of superconductors9WyCryst: Wyckoff inorganic crystal generator framework41Generative AI for Materials Discovery: Design Without Understanding11Crystal Composition Transformer: Self‐Learning Neural Language Model for Generative and Tinkering Design of Materials28Computational design of energy‐related materials: From first‐principles calculations to machine learning9Ionic species representations for materials informatics5Deep learning-driven evaluation and prediction of ion-doped NASICON materials for enhanced solid-state battery performance11Digital manufacturing of perovskite materials and solar cells12Concluding remarks: Faraday Discussion on data-driven discovery in the chemical sciences1Machine learning prediction of materials properties from chemical composition: Status and prospects29Crystal structure generation with autoregressive large language modeling157Deep reinforcement learning for inverse inorganic materials design59AI-Infused Multimodal Intelligence Computing Architecture for Sustainable Green Material Discovery and Eco-Performance Prediction0Charting the chemical space of Zintl phases with graph neural networks and bonding insights2Coherent collections of rules describing exceptional materials identified with a multi-objective optimization of subgroups2Establishing baselines for generative discovery of inorganic crystals10SynCoTrain: a dual classifier PU-learning framework for synthesizability prediction9A generative model for inorganic materials design498

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  4. Electronic structure design for nanoporous, electrically conductive zeolitic imidazolate frameworks

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  9. Perspective: Theory and simulation of hybrid halide perovskites

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  11. Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure

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