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RESEARCH PAPER · Computational Materials Science · 2019

Can machine learning find extraordinary materials?

Steven K. Kauwe, Jake Graser, Ryan Murdock, Taylor D. Sparks

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Can machine learning find extraordinary materials?OriginalIn silico investigation of Cu(In,Ga)Se 2 -based solar cells3Design space visualization for guiding investments in biodegradable and sustainably sourced materials7Compositionally-Restricted Attention-Based Network for Materials Property Prediction6Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509Accelerated design of Fe-based soft magnetic materials using machine learning and stochastic optimization136An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery94Machine learning enabled advanced manufacturing in nuclear engineering applications12Machine-learned metrics for predicting the likelihood of success in materials discovery38Is Domain Knowledge Necessary for Machine Learning Materials Properties?106Machine learning in materials design: Algorithm and application*62Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian process-based exploration–exploitation11A data science approach for advanced solid polymer electrolyte design16Interpretabilidade de modelos de aprendizado profundo aplicados ao diagnóstico e prognóstico não supervisionado de falhas.3Accelerating materials discovery using machine learning238A new material discovery platform of stable layered oxide cathodes for K-ion batteries78High-throughput virtual screening for organic electronics: a comparative study of alternative strategies68Digital Transformation in Materials Science: A Paradigm Change in Material's Development72Machine learning approach for the prediction and optimization of thermal transport properties71Evaluation of machine learning based models to predict the bulk density in the flash sintering process14Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion19Beyond Tripeptides Two-Step Active Machine Learning for Very Large Data sets50Machine Learning in Magnetic Materials48Machine learning and symbolic regression investigation on stability of MXene materials83Compositionally restricted attention-based network for materials property predictions260Machine Learning in Solid‐State Chemistry: Heusler Compounds0Sequential Machine Learning Applications of Particle Packing with Large Size Variations6DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions0DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions4CrabNet for explainable deep learning in materials science: bridging the gap between academia and industry0DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions3Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model53Optimizing Fractional Compositions to Achieve Extraordinary Properties15A deep learning augmented genetic algorithm approach to polycrystalline 2D material fracture discovery and design44Synthetic Data Augmentation of Cycling Sport Training Datasets4An Interpretable Boosting-Based Predictive Model for Transformation Temperatures of Shape Memory Alloys21Data Mining for Terahertz Generation Crystals0DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions28Prediction of Thermal Properties of Zeolites through Machine Learning50CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry31Can ensemble machine learning be used to predict the groundwater level dynamics of farmland under future climate: a 10-year study on Huaibei Plain35Data Mining for Terahertz Generation Crystals62Shape memory elastomers: A review of synthesis, design, advanced manufacturing, and emerging applications41Text Mining for Energy Materials3DeepBuckle: Extracting physical behavior directly from empirical observation for a material agnostic approach to analyze and predict buckling13The case for data science in experimental chemistry: examples and recommendations83Effect of reducible and irreducible search space representations on adaptive design efficiency: a case study on maximizing packing fraction for solid rocket fuel propellant simulations2The most compact search space is not always the most efficient: A case study on maximizing solid rocket fuel packing fraction via constrained Bayesian optimization6Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model48Generative multiscale analysis of de novo proteome-inspired molecular structures and nanomechanical optimization using a VoxelPerceiver transformer model15Data-driven materials discovery and synthesis using machine learning methods14Compound Knowledge Graph-Enabled AI Assistant for Accelerated Materials Discovery15A transferable recommender approach for selecting the best density functional approximations in chemical discovery31A Conceptual Distinction between Generalization and Transfer in Materials Machine Learning0Extrapolation is Not the Same as Interpolation5Quantifying the performance of machine learning models in materials discovery47Synthetic data enable experiments in atomistic machine learning16Predicting mechanical fields near cracks using a progressive transformer diffusion model and exploration of generalization capacity30Revealing Hidden Patterns through Chemical Intuition and Interpretable Machine Learning: A Case Study of Binary Rare-Earth Intermetallics RX16Compactness Matters: Improving Bayesian Optimization Efficiency of Materials Formulations through Invariant Search Spaces12Integrating Computation, Experiment, and Machine Learning in the Design of Peptide‐Based Supramolecular Materials and Systems61Integrating Computation, Experiment, and Machine Learning in the Design of Peptide‐Based Supramolecular Materials and Systems11A critical examination of robustness and generalizability of machine learning prediction of materials properties146The role of machine learning in perovskite solar cell research42Prospective on methods of design of experiments for limited data scenarios in materials design and engineering6Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach61In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99A critical review of future aspects of digitalization next generation Li-ion batteries manufacturing process38Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science4Towards physics-informed explainable machine learning and causal models for materials research34Not as simple as we thought: a rigorous examination of data aggregation in materials informatics13A Conceptual Theory of Measurement Validity for AI-Generated Materials Properties1Bias in Materials Datasets without Datasets: A Conceptual Account of How Bias Enters Before Any Modeling1Foundation Models in Materials Science: Emerging Architectures and Training Paradigms0Scientific Accountability for Materials AI: A Conceptual Standard for Reporting Claims and Limitations0Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning70Manufacturing process encoding through natural language processing for prediction of material properties15Experimentally validated inverse design of multi-property Fe-Co-Ni alloys30Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials10Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study66Extrapolation is not the same as interpolation25Toward accelerated discovery of solid catalysts using extrapolative machine learning approach10A critical reflection on attempts to machine-learn materials synthesis insights from text-mined literature recipes16Machine Learning Predictions of Methane Storage in MOFs: Diverse Materials, Multiple Operating Conditions, and Reverse Models8Machine learning assisted innovative strategy for experimental validation of functional properties of lead-free ceramics8Accelerating the Discovery of New, Single Phase High Entropy Ceramics via Active Learning11Exploring the impact of spatial autocorrelation on optimistic bias in cross-validation and assessing the effectiveness of spatial cross-validation15MatFold: systematic insights into materials discovery models' performance through standardized cross-validation protocols17Design of high-temperature NiTiCuHf shape memory alloys with minimum thermal hysteresis using Bayesian optimization15Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials4Classification of (dis)ordered structures as superionic lithium conductors with an experimental structure–conductivity database7Coherent collections of rules describing exceptional materials identified with a multi-objective optimization of subgroups2Comparative analysis of search approaches to discover donor molecules for organic solar cells0Introduction to Machine Learning for Predictive Modeling of Organic Materials0Out-of-Distribution Generalization in Materials AI: A Systematic Review of Domain Shift, Robustness, and What Remains Unsolved0Exceptionally optimized millimeter-wave properties of cordierite-based materials via innovative processing and predictive analytics7Assessing data to optimize soybean protein extraction6Recent developments in the use of machine learning in catalysis: A broad perspective with applications in kinetics69Extrapolative-machine-learning-guided discovery of multi-elemental heterogeneous catalysts for low-temperature NO reduction by H21Unsupervised Machine Learning Prediction of a Novel 1:3 Intermetallic Phase with the Synthesis of TbIr 3 (PuNi 3 -type) as Experimental Validation7Digitized material design and performance prediction driven by high-throughput computing2Artificial Intelligence‐assisted Heterogeneous Catalyst Design, Discovery, and Synthesis Utilizing Experimental Data1A review on electrolyte innovation enabled by machine learning for energy storage applications5Evaluating the use of a machine learning simulator for structure–property prediction: A case study on disordered elastic networks2Bridging artificial intelligence with photovoltaics7Locking of dendrites in potassium metal batteries: A potassiophilic 3D host discovered with machine learning screening2Integrated design framework for titanium aluminides through interpretable machine learning6Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules29Extrapolative-Machine-Learning-Guided Discovery of Multielemental Heterogeneous Catalysts for Low-Temperature NO Reduction by H 211Integrated Experimental and Machine Learning Approach for Reactive Black 5 Removal Using Straw-Derived Biochars2Approaching the reactivity of anions in battery electrolytes via conceptual density functional theory2Prospective Revisions on the Use of Machine Learning in the Aluminum Alloy Development: A Review on Techniques and Future Applications0Do's and Don'ts When Visiting Circularly Polarized Luminescence9A Review of Knowledge Base Construction Strategies for LLM-based Intelligent Decision Support System in Material Selection and Process Planning0Model Prediction and Multi-Objective Optimization of Unfired Bricks Incorporated with Drinking Water Treatment Sludge Using Machine Learning0Multi-model structure-agnostic framework for enhanced materials discovery in engineering informatics1Attention-based composition learning for thermodynamic and electronic property prediction in inorganic materials0Ranking-based machine learning framework for CO2 uptake screening in MOF-801 variants under sparse data conditions0From fidelity to modelability: Advancing electronic band gap prediction of materials with AI diagnostics0

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  1. In silico investigation of Cu(In,Ga)Se 2 -based solar cells

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  2. Design space visualization for guiding investments in biodegradable and sustainably sourced materials

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  3. Compositionally-Restricted Attention-Based Network for Materials Property Prediction

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  4. Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

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  5. Accelerated design of Fe-based soft magnetic materials using machine learning and stochastic optimization

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  6. An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery

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  7. Machine learning enabled advanced manufacturing in nuclear engineering applications

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  8. Machine-learned metrics for predicting the likelihood of success in materials discovery

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  9. Is Domain Knowledge Necessary for Machine Learning Materials Properties?

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  10. Machine learning in materials design: Algorithm and application*

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  11. Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian process-based exploration–exploitation

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  12. A data science approach for advanced solid polymer electrolyte design

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  14. Accelerating materials discovery using machine learning

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  15. A new material discovery platform of stable layered oxide cathodes for K-ion batteries

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  16. High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

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  18. Machine learning approach for the prediction and optimization of thermal transport properties

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  19. Evaluation of machine learning based models to predict the bulk density in the flash sintering process

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  21. Beyond Tripeptides Two-Step Active Machine Learning for Very Large Data sets

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  22. Machine Learning in Magnetic Materials

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  23. Machine learning and symbolic regression investigation on stability of MXene materials

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  24. Compositionally restricted attention-based network for materials property predictions

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  25. Machine Learning in Solid‐State Chemistry: Heusler Compounds

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  26. Sequential Machine Learning Applications of Particle Packing with Large Size Variations

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  31. Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model

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