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RESEARCH PAPER · Annual Review of Materials Research · 2020

Opportunities and Challenges for Machine Learning in Materials Science

Dane D. Morgan, Ryan Jacobs

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Opportunities and Challenges for Machine Learning in Materials ScienceOriginalMachine learning toward advanced energy storage devices and systems199Toward autonomous design and synthesis of novel inorganic materials159Opportunities and challenges of text mining in materials research180Machine learning for materials design and discovery128Machine learning in materials science: From explainable predictions to autonomous design281Polymer informatics: Expert-in-the-loop in QSPR modeling of refractive index30Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538Phase-field modeling of microstructure evolution: Recent applications, perspectives and challenges238An integrated texture analysis and machine learning approach for durability assessment of lightweight cement composites with hydrophobic coatings modified by nanocellulose29A deep learning based automatic defect analysis framework for In-situ TEM ion irradiations75An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference215Catalyze Materials Science with Machine Learning67Machine learning elastic constants of multi-component alloys68Autonomous experimentation systems for materials development: A community perspective398Adoption of Image-Driven Machine Learning for Microstructure Characterization and Materials Design: A Perspective30Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges67Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: A review113Graph network based deep learning of bandgaps20A Machine Learning-based surrogate modeling framework for predicting the history-dependent deformation of dual phase microstructures25Bayesian Machine Learning for Efficient Minimization of Defects in ALD Passivation Layers30Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data198Deep machine learning potentials for multicomponent metallic melts: Development, predictability and compositional transferability58Sustainable and efficient technologies for removal and recovery of toxic and valuable metals from wastewater: Recent progress, challenges, and future perspectives192Sparse modeling for small data: case studies in controlled synthesis of 2D materials26Data-Driven Methods for Accelerating Polymer Design123Machine learning in nuclear materials research125CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry31Highly accurate machine learning prediction of crystal point groups for ternary materials from chemical formula19The materials tetrahedron has a “digital twin”34Photoelectrochemical Properties, Machine Learning, and Symbolic Regression for Molecularly Engineered Halide Perovskite Materials in Water42Machine Learning in Materials Chemistry: An Invitation46Machine learning prediction of the mechanical properties of γ-TiAl alloys produced using random forest regression model112Machine learning combined with feature engineering to search for BaTiO3 based ceramics with large piezoelectric constant45Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys36Discovery and prediction capabilities in metal-based nanomaterials: An overview of the application of machine learning techniques and some recent advances47Machine learning in energy storage materials106Recent advances and applications of deep learning methods in materials science1161MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art70The case for data science in experimental chemistry: examples and recommendations83Data‐Driven Materials Innovation and Applications187Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels42Recent progress towards a universal machine learning model for reaction energetics in heterogeneous catalysis20Performance and limitations of deep learning semantic segmentation of multiple defects in transmission electron micrographs42The machine learning in lithium-ion batteries: A review45Calibration after bootstrap for accurate uncertainty quantification in regression models92Deep learning object detection in materials science: Current state and future directions82Machine learning in concrete science: applications, challenges, and best practices379Innovations Toward the Valorization of Plastics Waste65Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases22A Strategic Approach to Machine Learning for Material Science: How to Tackle Real-World Challenges and Avoid Pitfalls71When not to use machine learning: A perspective on potential and limitations51Towards out of distribution generalization for problems in mechanics30Machine learning for high-entropy alloys: Progress, challenges and opportunities397Machine learning versus human learning in predicting glass-forming ability of metallic glasses56Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology24Machine learning accelerates the materials discovery142Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks64Prediction of mechanical properties for deep drawing steel by deep learning2214 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon214Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials36Emerging Trends in Machine Learning: A Polymer Perspective187Analysis and evaluation of machine learning applications in materials design and discovery33Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170Rational design of high-entropy ceramics based on machine learning – A critical review52Understanding and Designing a High-Performance Ultrafiltration Membrane Using Machine Learning124Resolution-enhanced X-ray fluorescence microscopy via deep residual networks22A critical examination of robustness and generalizability of machine learning prediction of materials properties146Predicting Colorectal Cancer Using Machine and Deep Learning Algorithms: Challenges and Opportunities68Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives241Machine Learning-Enabled Superior Energy Storage in Ferroelectric Films with a Slush-Like Polar State22Prediction of mechanical properties of biomedical magnesium alloys based on ensemble machine learning38Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations24Machine learning-assisted crystal engineering of a zeolite58Band gap predictions of double perovskite oxides using machine learning96A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis62Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning37Needs, Trends, and Advances in Scintillators for Radiographic Imaging and Tomography98A General Tensor Prediction Framework Based on Graph Neural Networks22Outliers in Shannon’s effective ionic radii table and the table extension by machine learning34Data-driven machine learning for alloy research: Recent applications and prospects21AlphaMat: a material informatics hub connecting data, features, models and applications52Evolution of artificial intelligence for application in contemporary materials science27Property Prediction and Structural Feature Extraction of Polyimide Materials Based on Machine Learning28Development of chemistry-informed interpretable model for predicting compressive strength of recycled aggregate concrete containing supplementary cementitious materials28In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Machine Learning Reinforced Genetic Algorithm for Massive Targeted Discovery of Selectively Cytotoxic Inorganic Nanoparticles27From prediction to design: Recent advances in machine learning for the study of 2D materials90Machine Learning: Models, Challenges, and Research Directions87Organic pollutants removal from aqueous solutions using metal-organic frameworks (MOFs) as adsorbents: A review93Data augmentation using continuous conditional generative adversarial networks for regression and its application to improved spectral sensing20Recent progress in the JARVIS infrastructure for next-generation data-driven materials design38AI-enabled materials discovery for advanced ceramic electrochemical cells28Exploiting redundancy in large materials datasets for efficient machine learning with less data118Predictions and uncertainty estimates of reactor pressure vessel steel embrittlement using Machine learning27Advances of machine learning in materials science: Ideas and techniques105Identifying Stable Electrocatalysts Initialized by Data Mining: Sb 2 WO 6 for Oxygen Reduction26Formation energy prediction of crystalline compounds using deep convolutional network learning on voxel image representation23Machine‐Learning Assisted Screening Proton Conducting Co/Fe based Oxide for the Air Electrode of Protonic Solid Oxide Cell44Explainability and extrapolation of machine learning models for predicting the glass transition temperature of polymers23Latent Variable Machine Learning Framework for Catalysis: General Models, Transfer Learning, and Interpretability23A machine learning approach for the prediction of aqueous solubility of pharmaceuticals: a comparative model and dataset analysis43AlabOS: a Python-based reconfigurable workflow management framework for autonomous laboratories33Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions23Flexible, model-agnostic method for materials data extraction from text using general purpose language models42Machine learning for analyzing atomic force microscopy (AFM) images generated from polymer blends24The future of self-driving laboratories: from human in the loop interactive AI to gamification102Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets90Decoding Nanomaterial‐Biosystem Interactions through Machine Learning38Machine Learning Design of Perovskite Catalytic Properties83Extracting accurate materials data from research papers with conversational language models and prompt engineering354Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics64Multiscale Thermodynamics-Informed Neural Networks (MuTINN) towards fast and frugal inelastic computation of woven composite structures51Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations147Machine learning for analyses and automation of structural characterization of polymer materials43Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective60Hydrogen Embrittlement as a Conspicuous Material Challenge─Comprehensive Review and Future Directions405Investigating explainable transfer learning for battery lifetime prediction under state transitions47AI-Powered Knowledge Base Enables Transparent Prediction of Nanozyme Multiple Catalytic Activity36Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities157Machine intelligence accelerated design of conductive MXene aerogels with programmable properties72Machine learning-guided realization of full-color high-quantum-yield carbon quantum dots173The interface of machine learning and carbon quantum dots: From coordinated innovative synthesis to practical application in water control and electrochemistry88Different applications of machine learning approaches in materials science and engineering: Comprehensive review68A prospective on machine learning challenges, progress, and potential in polymer science30Machine learning-assisted accelerated research on piezoelectric response prediction of KNN-based ceramics27Review of the opportunities and limitations for powder-based high-throughput solid-state processing of advanced functional ceramics36Machine learning to enhance sustainable plastics: A review21Machine learning in materials research: Developments over the last decade and challenges for the future105Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review141Application of artificial intelligence in the materials science, with a special focus on fuel cells and electrolyzers78A prompt-engineered large language model, deep learning workflow for materials classification50When Metal Nanoclusters Meet Smart Synthesis31Exploring design space: Machine learning for multi-objective materials design optimization with enhanced evaluation strategies21A Review on Data-Driven Constitutive Laws for Solids142Machine learning-enhanced photocatalysis for environmental sustainability: Integration and applications53Machine learning-assisted design of high-performance perovskite photodetectors: a review22Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives92State-of-the-art review on various applications of machine learning techniques in materials science and engineering26AI4Materials: Transforming the landscape of materials science and enigneering25Harnessing DFT and machine learning for accurate optical gap prediction in conjugated polymers25Navigating the evolution of two-dimensional carbon nitride research: integrating machine learning into conventional approaches29Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration36On the preprocessing of physics-informed neural networks: How to better utilize data in fluid mechanics48Application of Machine Learning in Hydraulic Fracturing: A Review24A general approach for determining applicability domain of machine learning models56Topology optimization methods and its applications in aerospace: a review25Machine learning applications in SEM-based pore analysis: a review31Machine Learning Applications in Gray, Blue, and Green Hydrogen Production: A Comprehensive Review25Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories33Discovery of Stable Extra-large Pore Zeolites Based on Rational Design of Structure-directing Agents30

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