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RESEARCH PAPER · Annual Review of Materials Research · 2020

Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches

James E. Saal, Anton O. Oliynyk, Bryce Meredig

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Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying ApproachesOriginalMachine learning-guided design and development of metallic structural materials27Machine-Learning Interatomic Potentials for Materials Science397Materials informatics and sustainability—The case for urgency5A microstructure-informatic strategy for Vickers hardness forecast of austenitic steels from experimental data23Machine learning for advanced energy materials208A general and transferable deep learning framework for predicting phase formation in materials93Materials discovery through machine learning formation energy78Modeling of Alloying Effect on Isothermal Transformation: A Case Study for Pearlitic Steel18Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration65Machine learning for materials design and discovery128Application of high-throughput first-principles calculations in ceramic innovation101Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538Materials Representation and Transfer Learning for Multi-Property Prediction4Materials Representation and Transfer Learning for Multi-Property Prediction1Materials representation and transfer learning for multi-property prediction71Augmenting Adaptive Machine Learning with Kinetic Modeling for Reaction Optimization20Machine learning and evolutionary prediction of superhard B-C-N compounds68Autonomous experimentation systems for materials development: A community perspective398Machine learning for materials discovery: Two-dimensional topological insulators71Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals0Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals0Discovery of complex oxides via automated experiments and data science37Applied Machine Learning for Developing Next‐Generation Functional Materials72Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models44Analysis and Prediction of Hydrothermally Synthesized ZnO-Based Dye-Sensitized Solar Cell Properties Using Statistical and Machine-Learning Techniques28Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals0Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals0Application of machine learning in understanding the irradiation damage mechanism of high-entropy materials38Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals0Artificial intelligence: machine learning for chemical sciences104Applications of machine learning in computational nanotechnology13Discovery Acceleration vs Epistemic Depth: Speed–Understanding Trade-Offs in Computational Design0Representation Is Not Reality: Epistemic Limits of Learned Materials Embeddings in Computational Design Systems0The Illusion of Generalization: A Critique of Random Train-Test Splitting in Small-Crystal Property Prediction0Breaking the Aristotype: Featurization of Polyhedral Distortions in Perovskite Crystals14Active meta-learning for predicting and selecting perovskite crystallization experiments24Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design59Quantum alchemy beyond singlets: Bonding in diatomic molecules with hydrogen7Roadmap on Machine learning in electronic structure180A machine learning–based classification approach for phase diagram prediction51Machine learning in energy storage materials106MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art70Data-driven causal inference of process-structure relationships in nanocatalysis26Methodological framework for materials discovery using machine learning9Predicting tensorial molecular properties with equivariant machine learning models47Autonomous scanning probe microscopy investigations over WS2 and Au{111}32Green Chemistry Applied to Transition Metal Chalcogenides through Synthesis, Design of Experiments, Life Cycle Assessment, and Machine Learning5Machine Learning and First-Principles Discovery of Ternary Superhard Materials3Autonomous materials discovery and manufacturing (AMDM): A review and perspectives18Identification of high-dielectric constant compounds from statistical design14Crystal Structure Prediction via Efficient Sampling of the Potential Energy Surface68On the value of popular crystallographic databases for machine learning prediction of space groups14Prediction of Plasticizer Property Based on an Improved Genetic Algorithm3Data-driven materials discovery and synthesis using machine learning methods14Charge-density based convolutional neural networks for stacking fault energy prediction in concentrated alloys9What is a minimal working example for a self-driving laboratory?49Convolutional neural network-based colloidal self-assembly state classification10Quantifying the performance of machine learning models in materials discovery47Machine learning descriptors in materials chemistry: prediction and experimental validation synthesis of novel intermetallic UCd36On Uncertainty Quantification in Materials Modeling and Discovery: Applications of GE's BHM and IDACE7Analysis and evaluation of machine learning applications in materials design and discovery33Revealing Hidden Patterns through Chemical Intuition and Interpretable Machine Learning: A Case Study of Binary Rare-Earth Intermetallics RX16“Lubrication Brain” ― A machine learning framework of lubrication oil molecule design22Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering10Hybrid interatomic potential for Sn23A2BB’X6/ABX3-type High-performance perovskites screening based on ensemble learning and high throughput screening13A framework to predict binary liquidus by combining machine learning and CALPHAD assessments21Interpretable Machine Learning in Solid-State Chemistry, with Applications to Perovskites, Spinels, and Rare-Earth Intermetallics: Finding Descriptors Using Decision Trees18A coarse-grained study on mechanical behaviors of diamond-like carbon based on machine learning8Deep learning accelerates the development of Ni-based single crystal superalloys: A physical-constrained neural network for creep rupture life prediction37Steels classification by machine learning and Calphad methods16Hardness and fracture toughness models by symbolic regression19Hybrid Data‐Driven Discovery of High‐Performance Silver Selenide‐Based Thermoelectric Composites32In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Structure-Free Mendeleev Encodings of Material Compounds for Machine Learning18A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films19Quantifying the evolution of mechanical behaviours of concrete materials resting on realistic microstructural characteristics: A micro-to-macro validated combination analysis method43DSC - a dataset of superconductors including crystal structures26Thermal Stability and Mechanical Properties of Hollow Si Nanowires from Atomic Modeling Combined with a Machine-Learning Prediction for Application as Li-Ion Battery Anodes2Machine learning for structure-property mapping of Ising models: Scalability and limitations11A Prompt Engineering Approach for Structured Data Extraction from Unstructured Text Using Conversational LLMs16A Quantum-Classical Method Applied to Material Design: Photochromic Materials Optimization for Photopharmacology Applications0Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)23Flexible, model-agnostic method for materials data extraction from text using general purpose language models42Insights into Nanodiamond from Machine Learning1Navigation through high-dimensional chemical space: discovery of Ba 5 Y 13 [SiO 4 ] 8 O 8.5 and Ba 3 Y 2 [Si 2 O 7 ] 22Neural Networks for Prediction of Swelling in Austenitic Steels Irradiated by Fast Neurons0Neural Networks for Prediction of Swelling in Austenitic Steels Irradiated by Fast Neurons0Uncertainty-Aware Surrogate Modeling of High-Throughput DFT Data for Rapid Materials Screening0Predicting the oxidation kinetic rate and near-surface microstructural evolution of alumina-forming Ni-based single crystal superalloy based on machine learning22Structured information extraction from scientific text with large language models734Informatics-Driven Design of Superhard B–C–O Compounds4Extracting accurate materials data from research papers with conversational language models and prompt engineering354Machine learning accelerates the screening of single-atom catalysts towards CO2 electroreduction30Machine Learning Aided Design and Optimization of Thermal Metamaterials195Machine learning for CO 2 capture and conversion: A review60Classification of battery compounds using structure-free Mendeleev encodings12Strain sensing characteristics of 3D-printed carbon nanotubes/polypyrrole/UV-curable composites: experimental validation and machine learning predictions9Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective60Accurate space-group prediction from composition4Explorative prediction of novel superhard carbon allotropes with lager cell: Density functional theory-assisted deep learning10Maximum a posteriori estimation for high-throughput peak fitting in X-ray photoelectron spectroscopy4Transfer learning enables the rapid design of single crystal superalloys with superior creep resistances at ultrahigh temperature27Predicting Emission Wavelengths in Benzobisoxazole-Based OLEDs with Gradient Boosted Ensemble Models3Crystal Composition Transformer: Self‐Learning Neural Language Model for Generative and Tinkering Design of Materials28Machine Learning and Small Data-Guided Optimization of Silica Shell Morphology on Gold Nanorods10Machine learning in materials research: Developments over the last decade and challenges for the future105Neural networks for prediction of swelling in austenitic steels irradiated by fast neutrons5Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization8Machine Learning‐Enabled Nanoscale Phase Prediction in Engineered Poly(Vinylidene Fluoride)6Accelerating spin Hall conductivity predictions via machine learning6Analysis of the synergetic effect of process parameters of hydrogenated amorphous carbon deposition in plasma-enhanced chemical vapor deposition using machine learning4Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility17Kolmogorov–Arnold Network Made Learning Physics Laws Simple15Predicting homopolymer and copolymer solubility through machine learning10Large Language Model for Automating the Analysis of Cryoprotectants5Accelerating materials discovery through active learning: Methods, challenges and opportunities12Algorithmic Steering Without Consent: Control Asymmetries in AI-Directed Materials Exploration0Explainable ensemble learning to predict anisotropic nanomaterial band gap using atomic-scale structural descriptors2Many-body physics and machine learning enabled discovery of promising solar materials7The Problem of Unbounded Search Spaces in Conceptual Materials Discovery0Application of machine learning in adsorption energy storage using metal organic frameworks: A review17Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion28Multimodal foundation models for material property prediction and discovery42Machine Learning-Based Computational Design Methods for High-Entropy Alloys49An interpretable and reliable framework for alloy discovery in thermomechanical processing3Applications of density functional theory and machine learning in nanomaterials: A review51Rapid Discovery of Graphene Nanoflakes with Desired Absorption Spectra Using DFT and Bayesian Optimization with Neural Network Kernel6A review on the radial distribution function: Insights into molecular structure, intermolecular interactions, and thermodynamic properties85Enabling extrapolation of Young’s modulus of CaO-Al2O3-SiO2 ternary glasses by topology-informed machine learning3Artificial Intelligence in Computational and Materials Chemistry: Prospects and Limitations13Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories33Integrating AI into OLED material design: a comprehensive review of computational frameworks, challenges, and opportunities16Machine learning-guided optimization of Nb2CTx MXene preparation modified with tetrabutylammonium hydroxide: comprehensive structural characterization and enhanced performance analysis5Multiscale structure-mechanical property relationship and data-driven design of ultra-high temperature ceramics: A review7Chat-rgie: precision extraction of rice germplasm data using large language models and prompt engineering1Explainable Recommendation Engines to Predict Complex Intermetallics: Synthesis and Characterization of Gd10RuCd3, a Neutron Absorption Material1SIGD: symmetry and interface guided diffusion architectures for inverse design engineering of 2D semiconductor materials1Machine Learning Force Field Accelerated Prediction of Low Lattice Thermal Conductivity in Strained WS 2 Monolayer2Materials discovery through interpretation: An application to proton-conducting oxides2A deep recurrent learning framework for predicting saturation magnetic flux density in Fe-based metallic glasses1Extrapolative prediction of polymer properties using physics-informed hierarchical descriptors4The Predictive Power of Chemical Bonding Analysis in Materials: A Perspective on Optoelectronic Properties4Inverse design of high-performance glasses through an encoder-decoder machine learning approach toward materials discovery: Application to oxynitride glasses2A Curated Dataset of Tensile Properties for Cantor-Type Alloys0Clever materials: when models identify good materials for the wrong reasons0Data-driven exploration of AB 2 X 4 (X = O, S, Se, Te) spinel chemical space0Evaluating large language models for inverse semiconductor design0Machine Learning in Materials Science: Current Challenges and Future Outlook0Scientific Claims Under Distribution Shift: A Conceptual Theory for Robust Materials AI Inference0

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