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RESEARCH PAPER · Nature Chemistry · 2015

The quest for new functionality

Aron Walsh

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The quest for new functionalityOriginalBandgap Tunability in Sb‐Alloyed BiVO4 Quaternary Oxides as Visible Light Absorbers for Solar Fuel Applications42Material synthesis and design from first principle calculations and machine learning60Computational materials design of crystalline solids148Materials informatics: a journey towards material design and synthesis111On the application of the tolerance factor to inorganic and hybrid halide perovskites: a revised system1170Overview of the crystal chemistry of the actinide chalcogenides: incorporation of the alkaline-earth elements19Prediction of the dopant activity of chemical compounds against ammonia borane with key descriptors: electronegativity and crystal structures1Exploring long-wave infrared transmitting materials with AxBy form: First-principles gene-like studies7Large-scale calculations of gas phase thermochemistry: Enthalpy of formation, standard entropy, and heat capacity84Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathode27Toward Reaction-by-Design: Achieving Kinetic Control of Solid State Chemistry with Metathesis111Role of descriptors in predicting the dissolution energy of embedded oxides and the bulk modulus of oxide-embedded iron9Investigation of structural, magneto‐electronic, and thermoelectric response of ductile SnAlO3 from high‐throughput DFT calculations57Universal fragment descriptors for predicting properties of inorganic crystals653Perspective: Theory and simulation of hybrid halide perovskites140Perspective: Theory and simulation of hybrid halide perovskites86Machine learning for molecular and materials science4884Redesigning the Materials and Catalysts Database Construction Process Using Ontologies33Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery50AFLOW-CHULL: Cloud-Oriented Platform for Autonomous Phase Stability Analysis106General machine-learning surrogate models for materials prediction0Computational Design of Photovoltaic Materials4Machine learning, phase stability, and disorder with the Automatic Flow Framework for Materials Discovery0Creating Machine Learning-Driven Material Recipes Based on Crystal Structure46Machine Learning-Aided Design of Materials with Target Elastic Properties36MatCALO: Knowledge-enabled machine learning in materials science35Machine-learned multi-system surrogate models for materials prediction176Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods154Recent advances and applications of machine learning in solid-state materials science2604Influence of dopants oxidation states control on the photoluminescence properties of inorganic phospors0QSAR without borders875How strongly do Janus all-cis C6H6F6 and C6H6Cl6 bind ions in the gas-phase?4N2-dopant of graphene with electrochemically switchable bifunctional ORR/OER catalysis for Zn-air battery120The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design541Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders118Review: Simulation Models for Materials and Biomolecules5Predicting stable crystalline compounds using chemical similarity113Topological representations of crystalline compounds for the machine-learning prediction of materials properties112Machine Learning in Magnetic Materials48Artificial Intelligence Applied to Battery Research: Hype or Reality?501Data-driven thermoelectric modeling: Current challenges and prospects25Molecular representations for machine learning applications in chemistry94Toward machine learning for microscopic mechanisms: A formula search for crystal structure stability based on atomic properties7Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery551Artificial Intelligence and Advanced Materials117Direction-Based Graph Representation to Accelerate Stable Catalyst Discovery8Location of Artinite (Mg 2 CO 3 (OH) 2 ·3H 2 O) within the MgO–CO 2 –H 2 O system using ab initio thermodynamics5Strain data augmentation enables machine learning of inorganic crystal geometry optimization1Hybrid zinc‐air battery ( ZAB ) with transition metal‐based electrocatalysts—A step toward next‐generation electrochemical energy storage19Discerning the crystal structure and engineering the optoelectronic properties through substitution of divalent cations (M= Zn, N = Ge) in C3H3MNI3 for solar cell applications4Designing materials acceleration platforms for heterogeneous CO2 photo(thermal)catalysis28Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions38Recent advances and applications of machine learning in electrocatalysis36Navigating Materials Space with ML-Generated Electronic Fingerprints1ACGNet: An interpretable attention crystal graph neural network for accurate oxidation potential prediction12Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning0Applications of amorphous inorganics as novel functional materials38Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning0The importance of precise and suitable descriptors in data‐driven approach to boost development of lithium batteries: A perspective12Navigating Materials Space with ML-Generated Electronic Fingerprints3Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning7Computational Approaches to Guide the Synthesis of Lead-Free Perovskite Photocatalysts0Machine learning-enhanced analysis of catalyst particle size effects and performance prediction of platinum on carbon electrocatalysts8MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials6Generative Deep Learning for Advanced Battery Materials3High- T c superconductor candidates proposed by machine learning2Carbon-based transition metal chalcogenides for wearable sensors, energy storage, and photodetector applications2Generative Artificial Intelligence Navigated Development of Solvents for Next Generation High‐Performance Magnesium Batteries17MuAPBEK: An improved analytical kinetic energy density functional for quantum chemistry1Self-optimizing machine learning potential assisted automated workflow for highly efficient complex systems material design13From Screening to Generation: Reshaping the Paradigm of New Energy Materials Discovery With Artificial Intelligence2Navigating the Materials Space with Machine-Learning-Generated Electronic Fingerprints0Machine Learning Meets Photocatalytic Water Splitting for Sustainable Hydrogen Production0

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  1. Bandgap Tunability in Sb‐Alloyed BiVO4 Quaternary Oxides as Visible Light Absorbers for Solar Fuel Applications

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  2. Material synthesis and design from first principle calculations and machine learning

    Computational Materials Science · 2015-11-30

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  3. Computational materials design of crystalline solids

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    Keith T. Butler, Jarvist Moore Frost, Jonathan Michael Skelton, Katrine Louise Svane, Aron Walsh

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  4. Materials informatics: a journey towards material design and synthesis

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  5. On the application of the tolerance factor to inorganic and hybrid halide perovskites: a revised system

    Chemical Science · 2016-01-01

    Will Travis, Emily N. K. Glover, Hugo A. Bronstein, David O. Scanlon, Robert G. Palgrave

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  6. Overview of the crystal chemistry of the actinide chalcogenides: incorporation of the alkaline-earth elements

    Dalton Transactions · 2016-01-01

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  7. Prediction of the dopant activity of chemical compounds against ammonia borane with key descriptors: electronegativity and crystal structures

    New Journal of Chemistry · 2016-01-01

    KEISUKE G. TAKAHASHI, Yuki Nakagawa, Lauren Takahashi, Shigehito Isobe

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  8. Exploring long-wave infrared transmitting materials with AxBy form: First-principles gene-like studies

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  9. Large-scale calculations of gas phase thermochemistry: Enthalpy of formation, standard entropy, and heat capacity

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  10. Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathode

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  11. Toward Reaction-by-Design: Achieving Kinetic Control of Solid State Chemistry with Metathesis

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    Andrew J. Martinolich, James Richmond Neilson

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  12. Role of descriptors in predicting the dissolution energy of embedded oxides and the bulk modulus of oxide-embedded iron

    Physical review. B./Physical review. B · 2017-01-04

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  13. Investigation of structural, magneto‐electronic, and thermoelectric response of ductile SnAlO3 from high‐throughput DFT calculations

    International Journal of Quantum Chemistry · 2017-02-07

    Shakeel Ahmad Khandy, Dinesh C. Gupta

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  14. Universal fragment descriptors for predicting properties of inorganic crystals

    Nature Communications · 2017-06-05

    Olexandr Isayev, Corey Oses, Cormac Toher, Eric Gossett, Stefano Curtarolo, Alexander Tropsha

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  15. Perspective: Theory and simulation of hybrid halide perovskites

    The Journal of Chemical Physics · 2017-06-08

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  16. Perspective: Theory and simulation of hybrid halide perovskites

    Northumbria Research Link (Northumbria University) · 2017-06-14

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  17. Machine learning for molecular and materials science

    Nature · 2018-07-01

    Keith T. Butler, Daniel W. Davies, Hugh Cartwright, Olexandr Isayev, Aron Walsh

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  18. Redesigning the Materials and Catalysts Database Construction Process Using Ontologies

    Journal of Chemical Information and Modeling · 2018-08-02

    Lauren Takahashi, Itsuki Miyazato, KEISUKE G. TAKAHASHI

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  19. Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery

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  20. AFLOW-CHULL: Cloud-Oriented Platform for Autonomous Phase Stability Analysis

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  21. General machine-learning surrogate models for materials prediction

    arXiv (Cornell University) · 2018-09-24

    Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker, Alexander V. Shapeev, Tim Mueller, Conrad W. Rosenbrock, Gábor Cśanyi, David Wingate, Gus L. W. Hart

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  22. Computational Design of Photovoltaic Materials

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  23. Machine learning, phase stability, and disorder with the Automatic Flow Framework for Materials Discovery

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  24. Creating Machine Learning-Driven Material Recipes Based on Crystal Structure

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  25. Machine Learning-Aided Design of Materials with Target Elastic Properties

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  26. MatCALO: Knowledge-enabled machine learning in materials science

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  27. Machine-learned multi-system surrogate models for materials prediction

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  28. Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods

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  30. Influence of dopants oxidation states control on the photoluminescence properties of inorganic phospors

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  32. How strongly do Janus all-cis C6H6F6 and C6H6Cl6 bind ions in the gas-phase?

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  33. N2-dopant of graphene with electrochemically switchable bifunctional ORR/OER catalysis for Zn-air battery

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  35. Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

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  36. Review: Simulation Models for Materials and Biomolecules

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  37. Predicting stable crystalline compounds using chemical similarity

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  38. Topological representations of crystalline compounds for the machine-learning prediction of materials properties

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  39. Machine Learning in Magnetic Materials

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  40. Artificial Intelligence Applied to Battery Research: Hype or Reality?

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  47. Location of Artinite (Mg 2 CO 3 (OH) 2 ·3H 2 O) within the MgO–CO 2 –H 2 O system using ab initio thermodynamics

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  48. Strain data augmentation enables machine learning of inorganic crystal geometry optimization

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  49. Hybrid zinc‐air battery ( ZAB ) with transition metal‐based electrocatalysts—A step toward next‐generation electrochemical energy storage

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  50. Discerning the crystal structure and engineering the optoelectronic properties through substitution of divalent cations (M= Zn, N = Ge) in C3H3MNI3 for solar cell applications

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