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RESEARCH PAPER · Nature Communications · 2021

Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry

Andrij Vasylenko, Jacinthe Gamon, Benjamin B. Duff, Vladimir V. Gusev, Matthew J. Rosseinsky, Marco Zanella, J. Felix Shin, Paul M. Sharp, Alexandra Morscher, Ruiyong Chen, Alex Ryan Neale, Laurence J. Hardwick, John B. Claridge, Frédéric Blanc, Michael W. Gaultois, Matthew Stephen Dyer, Matthew J. Rosseinsky

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Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistryOriginalAn invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties189A Generalized Thermophysical Model for Materials from Molecular Clusters to Bulk Crystals3Machine Learning Utilized for the Development of Proton Exchange Membrane Electrolyzers51Predicting spinel solid solutions using a random atom substitution method8Toward autonomous materials research: Recent progress and future challenges60A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery48Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties5An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties18Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation118Opportunities for machine learning to accelerate halide-perovskite commercialization and scale-up35Progress in lithium thioborate superionic conductors6Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning47Neural network training method for materials science based on multi-source databases17Robotic Powder Grinding with a Soft Jig for Laboratory Automation in Material Science20Halide solid-state electrolytes for all-solid-state batteries: structural design, synthesis, environmental stability, interface optimization and challenges133A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning99Rational design of high-entropy ceramics based on machine learning – A critical review52Bioinspired inhibition of aggregation in metal-organic frameworks (MOFs)14Evolutionary Algorithm Directed Synthesis of Mixed Anion Compounds LaF2X (X=Br, I) and LaFI28Evolutionary Algorithm Directed Synthesis of Mixed Anion Compounds LaF2X (X=Br, I) and LaFI22Investigation of the Wear Behavior of AA6063/Zirconium Oxide Nanocomposites Using Hybrid Machine Learning Algorithms10Oxide Ion-Conducting Materials Containing Tetrahedral Moieties: Structures and Conduction Mechanisms94Material symmetry recognition and property prediction accomplished by crystal capsule representation19Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties15Machine-Learning-Accelerated Screening of Double-Atom/Cluster Electrocatalysts for the Oxygen Reduction Reaction27Accelerating the prediction of stable materials with machine learning104Unleashing the Potential of NASICON Materials for Solid-State Batteries4Accelerated Experimental Design Using a Human-Ai Teaming Framework0Accelerating metal–organic framework discovery via synthesisability prediction: the MFD evaluation method for one-class classification models3Inverse design of semiconductor materials with deep generative models26Ontology-Driven Materials AI: A Conceptual Proposal for Making Materials Knowledge Machine-Actionable0Machine learning‐assisted search for novel coagulants: When machine learning can be efficient even if data availability is low10Battery safety: Machine learning-based prognostics210Towards AI Research Agents in the Chemical Sciences3First-Principles-Based Kinetic Monte Carlo Model of Hydrogen Evolution Reaction under Realistic Conditions: Solvent, Hydrogen Coverage and Electric Field Effects46Inferring energy–composition relationships with Bayesian optimization enhances exploration of inorganic materials6Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte143Superionic lithium transport via multiple coordination environments defined by two-anion packing74Methods and applications of machine learning in computational design of optoelectronic semiconductors22Statistically Derived Proxy Potentials Accelerate Geometry Optimization of Crystal Structures2Interface engineering to construct heterostructured SnS2/Sb2S3 on rGO through a targeted-complexation deposition strategy for high-performance lithium-ion capacitors15A Facile In Situ Sulfurization Strategy for Heterostructured SnS 2 @Graphene Scrolls Anode with Enhanced Initial Coulombic Efficiency for High‐Energy Lithium Storage11Unsupervised learning of charge-discharge cycles from various lithium-ion battery cells to visualize dataset characteristics and to interpret model performance11Confluence of electronic structure calculations (DFT) and machine learning (ML) for lithium and sodium-ion batteries: a theoretical perspective8Bayesian optimization acquisition functions for accelerated search of cluster expansion convex hull of multi-component alloys15Machine Learning Discovers Invariants of Braids and Flat Braids0Data driven studies of magnetic ground state and transition temperature in two-dimensional magnets2Artificial Intelligence and Generative Models for Materials Discovery: A Review14Composition and structure analyzer/featurizer for explainable machine-learning models to predict solid state structures13Data-driven atomistic modeling of crystalline and glassy solid-state electrolytes6Digital features of chemical elements extracted from local geometries in crystal structures2Establishing baselines for generative discovery of inorganic crystals10Immobilization of SnS2/SnO2 heterostructures by rGO for high-capacity lithium storage: A facile thiostannate conversion-oxygen substitution synergistic route3Simultaneous Exploration of Candidates for Electrode and Electrolyte Materials for All-Solid-State Batteries Using Predicted Rating from a Recommender System2AI-empowered digital design of zeolites: Progress, challenges, and perspectives15Elemental Reactivity Maps for Materials Discovery1Accelerated experimental design using a human–AI teaming framework6Crystal Structure Prediction0Discovery of Crystalline Inorganic Solids in the Digital Age4Unsupervised Machine Learning Prediction of a Novel 1:3 Intermetallic Phase with the Synthesis of TbIr 3 (PuNi 3 -type) as Experimental Validation7Latent retinal structural patterns with aging4Interpretable Machine Learning Applications: A Promising Prospect of AI for Materials68Topology Augmented with Geometry in the Assembly of Structural Databases: Kagome Intermetallics3Machine learning pipelines for the design of solid-state electrolytes30Enabling knowledge discovery from data0Informatics for thermodynamics: Stability to synthesizability0Low-dimensional carbon materials to diamond-like phase transitions under extreme conditions: mechanisms, thermodynamic properties and applications2Next-Generation Electrolytes for Solid-State Batteries0Perspectives Towards AI and ML0Computational Discovery of Li 6 PO 5 I: An Oxide Argyrodite for Solid-State Electrolytes3Atomic-scale observation of vacancy ordering in magnetite nanoparticles0Crystal structure and property predictions of gallium phosphide via generative adversarial networks1Data-driven strategies for comprehending the kinetic degradation of inorganic solid-state electrolytes in lithium-based batteries: A review0Data driven intelligent research and development of gel electrolyte: Methods, challenges and cutting-edge trends1AI-accelerated realization of next-generation batteries: status quo and outlook3When Trees Guide Molecules: Multiobjective Search in de Novo Drug Design1Octahedral-motif-guided design of optoelectronic semiconductors via interpretable machine learning0Machine Learning and Theoretical Computation Synergy Advancing Halide Electrolytes Toward All‐Solid‐State Lithium Batteries: Recent Advances, Challenges, and Perspectives1The diffusion of artificial intelligence methods in scientific research: growth pattern, disciplinary variation, and task alignment0Fluoride‐Driven Interfacial Engineering: Architecting Functional Layers for High‐Performance Dendrite‐Free Li Metal Batteries0

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  2. A Generalized Thermophysical Model for Materials from Molecular Clusters to Bulk Crystals

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  3. Machine Learning Utilized for the Development of Proton Exchange Membrane Electrolyzers

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  8. An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties

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  9. Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

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  10. Opportunities for machine learning to accelerate halide-perovskite commercialization and scale-up

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  11. Progress in lithium thioborate superionic conductors

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  12. Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning

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  13. Neural network training method for materials science based on multi-source databases

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  17. Rational design of high-entropy ceramics based on machine learning – A critical review

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  19. Evolutionary Algorithm Directed Synthesis of Mixed Anion Compounds LaF2X (X=Br, I) and LaFI2

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  21. Investigation of the Wear Behavior of AA6063/Zirconium Oxide Nanocomposites Using Hybrid Machine Learning Algorithms

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