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RESEARCH PAPER · New Journal of Physics · 2017

Uncovering structure-property relationships of materials by subgroup discovery

Bryan R. Goldsmith, Mario Boley, Jilles Vreeken, Matthias Scheffler, Luca M. Ghiringhelli

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Uncovering structure-property relationships of materials by subgroup discoveryOriginalDiscovering Reliable Approximate Functional Dependencies24Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery19Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery33Machine Learning and Materials Informatics: Recent Applications and Prospects8Discovering Reliable Approximate Functional Dependencies49Beyond Ordered Materials: Understanding Catalytic Sites on Amorphous Solids195Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups4Telling Cause from Effect Using MDL-Based Local and Global Regression34Machine learning in materials informatics: recent applications and prospects1914Identifying models of dielectric breakdown strength from high-throughput data via genetic programming30SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates269Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation29Acid strength of zeolitic Brønsted sites—Dependence on dielectric properties32Machine learning for heterogeneous catalyst design and discovery444Neural-network-enhanced evolutionary algorithm applied to supported metal nanoparticles110Identifying key factors of student academic performance by subgroup discovery54Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning388Polar projections for big data analysis in applied superconductivity13SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates824Predicting Monolayer Oxide Stability over Low-Index Surfaces of TiO2 Polymorphs Using ab Initio Thermodynamics9NOMAD: The FAIR concept for big data-driven materials science453Discovery of Descriptors for Stable Monolayer Oxide Coatings through Machine Learning17Committee machine that votes for similarity between materials5Identifying flight delay patterns using diverse subgroup discovery11Telling cause from effect by local and global regression17Big Data-Driven Materials Science and Its FAIR Data Infrastructure22Making Machine Learning Work in Chemistry: Methodological Innovation, Software Development, and Application Studies3Prediction of Mohs Hardness with Machine Learning Methods Using Compositional Features4Two-to-three dimensional transition in neutral gold clusters: The crucial role of van der Waals interactions and temperature56Materials informatics: From the atomic-level to the continuum191Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO185From DFT to machine learning: recent approaches to materials science–a review849Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators13High-throughput assessment of hypothetical zeolite materials for their synthesizeability and industrial deployability20The NOMAD laboratory: from data sharing to artificial intelligence352Thinking Globally, Acting Locally: On the Issue of Training Set Imbalance and the Case for Local Machine Learning Models in Chemistry6Thinking Globally, Acting Locally: On the Issue of Training Set Imbalance and the Case for Local Machine Learning Models in Chemistry2Data-driven crack assessment based on surface measurements4Data‐Driven Materials Science: Status, Challenges, and Perspectives700Thinking Globally, Acting Locally: On the Issue of Training Set Imbalance and the Case for Local Machine Learning Models in Chemistry12Viewpoint: Atomic-Scale Design Protocols toward Energy, Electronic, Catalysis, and Sensing Applications39Big Data-Driven Materials Science and Its FAIR Data Infrastructure38Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity123Materials Representations1Structural Tolerance Factor Approach to Defect-Resistant I2-II-IV-X4 Semiconductor Design38Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509Recent advances in knowledge discovery for heterogeneous catalysis using machine learning130Discovering dependencies with reliable mutual information8Chemical Batteries with CO250Chemische Batterien mit CO21Discovering Approximate Functional Dependencies using Smoothed Mutual Information14Discovering Functional Dependencies from Mixed-Type Data10Wiz: A Web-Based Tool for Interactive Visualization of Big Data20Data-driven studies of magnetic two-dimensional materials76Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys100Critical review of machine learning applications in perovskite solar research139The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions69Computational compound screening of biomolecules and soft materials by molecular simulations0A Unified Research Data Infrastructure for Catalysis Research – Challenges and Concepts79Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence226Nanocluster heterogeneous catalysts: Insights from theory1Subgroup Discovery Points to the Prominent Role of Charge Transfer in Breaking Nitrogen Scaling Relations at Single-Atom Catalysts on VS257Catalyze Materials Science with Machine Learning67Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery0Artificial-intelligence-driven discovery of catalyst “genes” with application to CO2 activation on semiconductor oxides5Identifying Outstanding Transition-Metal-Alloy Heterogeneous Catalysts for the Oxygen Reduction and Evolution Reactions via Subgroup Discovery21Data analytics accelerates the experimental discovery of new thermoelectric materials with extremely high figure of merit2Exceptional model mining meets multi-objective optimization : Application to plant growth recipes in controlled environments0Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery7"What makes my queries slow?": Subgroup Discovery for SQL Workload Analysis14Screening of potential double perovskite materials for photovoltaic applications using agglomerative hierarchical clustering1Interpretable Machine Learning of Chemical Bonding at Solid Surfaces49Rational design of heterogeneous catalysts by breaking and rebuilding scaling relations8Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides145Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence54Roadmap on Machine learning in electronic structure180Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors122Interpretable machine learning for knowledge generation in heterogeneous catalysis428Machine learning of material properties: Predictive and interpretable multilinear models114High-Throughput Screening of Stable Single-Atom Catalysts in CO 2 Reduction Reactions101Improving Symbolic Regression for Predicting Materials Properties with Iterative Variable Selection62Robust subgroup discovery16Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137Advancing descriptor search in materials science: feature engineering and selection strategies8A Route Map of Machine Learning Approaches in Heterogeneous CO2 Reduction Reaction27Evidence-based data mining method to reveal similarities between materials based on physical mechanisms4Electronic Descriptors for Supervised Spectroscopic Predictions7Machine Learning Descriptors for Data‐Driven Catalysis Study154A state-of-the-art review on the utilization of machine learning in nanofluids, solar energy generation, and the prognosis of solar power44Effective subgrouping enhances machine learning prediction in complex materials science phenomena: Inoue's subgrouping in discovering bulk metallic glasses16Data-Driven Design of High-Curie Temperature Full-Heusler Alloys for Spintronic Applications0Unraveling the formation of oxygen vacancies on the surface of transition metal-doped ceria utilizing artificial intelligence20Unveiling the physical mechanisms driving delafossite crystal (ABX 2 ) formation through interpretable machine learning2Advances in materials informatics: a review42Roadmap on Data-Centric Materials Science0Roadmap on Data-Centric Materials Science1Roadmap on Data-Centric Materials Science1Roadmap on Data-Centric Materials Science2Roadmap on data-centric materials science27OPTIMIZING SQL DATABASES FORBIG DATA WORKLOADS: TECHNIQUES AND BEST PRACTICES15Data-driven design of high-curie temperature full-heusler alloys for spintronic applications5An interpretable predictive framework based on rules and machine learning: rule extraction, validation and adaptive integration with machine learning0Practical Application of Machine Learning in Catalysis1Coherent collections of rules describing exceptional materials identified with a multi-objective optimization of subgroups2MXenes and its composite structures: synthesis, properties, applications, 3D/4D printing, and artificial intelligence; machine learning integration78Rules Describing CO 2 Activation on Single-Atom Alloys from DFT-Meta-GGA Calculations and Artificial Intelligence13Artificial-intelligence-assisted design principle for developing high-performance single-atom catalysts22Advancing the understanding and prediction accuracy of molecular adsorption energy with artificial intelligence1Identifying Key Factors Influencing Advanced Hydrogen Evolution Reaction Catalysts5Subgroup discovery similarity score (SDSS): A significant criterion for the integration of statistical knowledge into machine learning in materials science0Modeling Time-On-Stream Catalyst Reactivity in the Selective Hydrogenation of Concentrated Acetylene Streams under Industrial Conditions via Experiments and AI5Dynamic catalysts and the digital age: Synthetic consequences2Ambient-condition hydrogen storage: Accelerating the development of high-capacity hydrides via a quantitative interpretable machine learning framework6Surface Coordination-Promoted Electrosynthesis of 1,4-Butanediol from Ethylene and Water via 2-Bromoethanol0Unveiling nonlinear descriptors via hierarchical learning for single-atom catalysts1MatSub: A performance-oriented subgroup discovery framework for materials informatics3Electron-Counting Controls Bifunctional Activity in Single-Atom Catalysts through a Three-Regime Adsorption Mechanism1Mapping Distinct Oxygen‐Binding Regimes in Single‐Atom Alloys0Polaronic Lattice State Hybridization in Reduced Oxide Surface Reconstructions0From fidelity to modelability: Advancing electronic band gap prediction of materials with AI diagnostics0

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  2. Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery

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  3. Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery

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  4. Machine Learning and Materials Informatics: Recent Applications and Prospects

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  6. Beyond Ordered Materials: Understanding Catalytic Sites on Amorphous Solids

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  7. Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups

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  8. Telling Cause from Effect Using MDL-Based Local and Global Regression

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  9. Machine learning in materials informatics: recent applications and prospects

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  10. Identifying models of dielectric breakdown strength from high-throughput data via genetic programming

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  11. SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

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  18. Polar projections for big data analysis in applied superconductivity

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  20. Predicting Monolayer Oxide Stability over Low-Index Surfaces of TiO2 Polymorphs Using ab Initio Thermodynamics

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  22. Discovery of Descriptors for Stable Monolayer Oxide Coatings through Machine Learning

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  23. Committee machine that votes for similarity between materials

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  24. Identifying flight delay patterns using diverse subgroup discovery

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  25. Telling cause from effect by local and global regression

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