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RESEARCH PAPER · Materials Today Communications · 2022

Machine learning accelerates the materials discovery

Jiheng Fang, Ming Xie, Xingqun He, Jiming Zhang, Jieqiong Hu, Yongtai Chen, Youcai Yang, Qinglin Jin

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Machine learning accelerates the materials discoveryOriginalStructure genome based machine learning method for woven lattice structures24Constructing the composition ratio prediction model using machine learning for BaSi2 thin films deposited by thermal evaporation2Fine-Tuning Nanoscale Vibrational Interactions of Low-Dimensional Nanocarbon Multilayer Transition Interfaces in 3D-Printed Extreme Lattice Metamaterials1Key Parameters and Optimal Design of a Split Induction Coil for T-Shaped Pipe Brazing4Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning37Accurate prediction of semiconductor bandgaps based on machine learning and prediction of bandgaps for two-dimensional heterojunctions14Inverse design of 3D cellular materials with physics-guided machine learning703D-printed microfluidic system for the in situ diagnostics and screening of nanoparticles synthesis parameters16Physics-informed machine learning with high-throughput design module for evaluating rupture life and guiding design of oxide/oxide ceramic matrix composites18Property Prediction and Structural Feature Extraction of Polyimide Materials Based on Machine Learning28Application of Machine Learning in Material Synthesis and Property Prediction166Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations32X-ray Diffraction Data Analysis by Machine Learning Methods—A Review100Artificial intelligence analysis of the sterilization effects on the mechanical properties of human cranial bones2From prediction to design: Recent advances in machine learning for the study of 2D materials90Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives10Smart manufacturing with transfer learning under limited data: Towards Data-Driven Intelligences8Physics-Guided Neural Network with Gini Impurity-Based Structural Optimizer for Prediction of Membrane-Type Acoustic Material Transmission Loss0Multi-target regression via target combinations using principal component analysis12A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering15Machine learning models for capacitance prediction of porous carbon-based supercapacitor electrodes21A comprehensive review of covalent organic frameworks (COFs) and their derivatives in environmental pollution control327A Theory of Reproducibility for AI-Generated Materials Insights: What Must Be True for Claims to Hold0HARNESSING PHONON WAVE RESONANCE IN CARBYNE-ENRICHED NANO-INTERFACES TO ENHANCE ENERGY RELEASE IN NANOENERGETIC MATERIALS1Predicting the Tensile Strength of Steel Based on Different Deep Learning Models0The Burning Loss Behavior of Titanium Element in Electroslag Remelting Process and the Prediction of Titanium Content at the End Point Were Studied0Machine-learning potentials for nanoscale simulations of tensile deformation and fracture in ceramics30New Directions for Thermoelectrics: A Roadmap from High‐Throughput Materials Discovery to Advanced Device Manufacturing32Machine learning insights into CaCO3 phase transitions: Synthesis and phase prediction19High‐Entropy Oxides for Rechargeable Batteries105Reply to comment on ‘Composition-based aluminum alloy selection using an artificial neural network’0The Adjustment Analysis Method of the Active Surface Antenna Based on Convolutional Neural Network5Customizable anisotropic microlattices for additive manufacturing: Machine learning accelerated design, mechanical properties and structural-property relationships16Illustrating an Effective Workflow for Accelerated Materials Discovery14Exploring the Future of Polyhydroxyalkanoate Composites with Organic Fillers: A Review of Challenges and Opportunities29Machine learning and experimental study on a novel Cr–Mo–V–Ti high manganese steel: Microstructure, mechanical properties and abrasive wear behavior12Machine Learning‐Based Prediction of Supercapacitor Capacitance for MgCo2O4 Electrodes23Performance Characterization and Composition Design Using Machine Learning and Optimal Technology for Slag–Desulfurization Gypsum-Based Alkali-Activated Materials5Influence of Processing Parameters on Mechanical Properties and Degree of Crystallization of Polylactide7The future of bone regeneration: Artificial intelligence in biomaterials discovery24Prediction of titanium burn-off and untimate titanium content in electroslag process5Molecular dynamics simulation of alloying characteristics of Al–Mg nanoparticles under different process heating conditions5Unveiling the quantitative relationship between microstructural features and quasi-static tensile properties in dual-phase titanium alloys based on data-driven neural networks7Predicting High‐Performance Thermoelectric Materials With StarryData28State-of-art review on the process-structure-properties-performance linkage in wire arc additive manufacturing95Applying AI in the Area of Automation Systems: Overview and Challenges1Revolutionizing air pollution control: The role of single atom catalysts in mitigating emissions2Advancements in π‐Magnetism and Precision Engineering of Carbon‐Based Nanostructures12High‐temperature ablation resistance prediction of ceramic coatings using machine learning101XGBoost-based prediction of electrical properties for anode aluminium foil4How to choose proper electron acceptor groups for highly efficient copper electrolyte-based dye-sensitized solar cells?5A review of the preparation and prospects of amorphous alloys by mechanical alloying24Deep Learning and Machine Learning for Materials Design3Recent Advances in High‐Entropy Layered Oxide Cathode Materials for Alkali Metal‐Ion Batteries117RETRACTED: Exploring high entropy alloys: A review on thermodynamic design and computational modeling strategies for advanced materials applications95Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook31Enhancing creep rupture life prediction of high‐temperature titanium alloys using convolutional neural networks5A data-driven uncertainty quantification framework in probabilistic bio-inspired porous materials (Material-UQ): An investigation for RotTMPS plates40A machine learning method approach for designing novel high strength and plasticity metastable β titanium alloys47Accelerating materials property discovery in uncharted domains through the integration of high-throughput computation and machine learning2Advances in CO 2 capture and utilization: the role of DFT in understanding CO 2 activation and its conversion mechanisms for methanol and cyclic carbonates production12Artificial intelligence-driven revolution in nanozyme design: from serendipity to rational engineering59Experimental and modelling study on optimization of glass fiber filter diversion structure0Integrating ML Approaches for Photodetectors0Machine Learning Algorithms for Applications in Materials Science I2Machine learning approach for surface morphology of nanoparticles: Image analysis and data mining techniques1Reactivity descriptors for sulfur redox kinetics in lithium–sulfur batteries: from mechanistic insights to machine learning driven catalyst design74Co-based spinel and perovskite oxides in catalytic combustion of volatile organic compounds: Recent advances and future prospects13Application of machine learning in polyimide structure design and property regulation5Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel3Predicting the magnetic properties of Fe-based bulk metallic glasses by ensemble machine learning and interpretable information5Machine Learning Speeds Up the Discovery of Efficient Porphyrinoid Electrocatalysts for Ammonia Synthesis20AI in single-atom catalysts: a review of design and applications28A machine learning-based framework for predicting the power factor of thermoelectric materials7Prediction of Composite Performance of Refractory and Ceramic Materials Based on Deep Learning3Optimizing Boride Coating Thickness on Steel Surfaces Through Machine Learning: Development, Validation, and Experimental Insights7Study on the Effects of Tar Reforming and Steam Gasification of Keyaki Bark in Saitama Prefecture1Design and optimization of a mechanical metamaterial featuring dual tunability in auxeticity and bandgap modulation14Prediction of thermal conductivity in multi-component magnesium alloys based on machine learning and multiscale computation15Prediction for magnetic properties of sintered NdFeB magnets based on machine learning7Machine learning-driven design of rare metal doped niobium alloys with enhanced strength and ductility6Deep learning enhanced prediction of microwave dielectric constant of spinel ceramics eliminating manual feature engineering4Accelerated material discovery of high-performance Mg alloys via active learning and high throughput multi-objective informed Bayesian optimization7High-throughput design strategy for creep-resistance steel using machine learning and optimization algorithm0Machine learning-assisted interfacial modulation and configuration design of metal matrix composites: A review8Machine learning-assisted screening of transition metal-doped TMDs for binding energy and charge transfer prediction2Time-series prediction of the deformation behavior of small circular ring based on residual structure and Bi-LSTM network0Rapid Discovery of Graphene Nanoflakes with Desired Absorption Spectra Using DFT and Bayesian Optimization with Neural Network Kernel6Biomimetic Intelligent Thermal Management Materials: From Nature‐Inspired Design to Machine‐Learning‐Driven Discovery50Predicting thermophysical properties and fracture toughness of Zirconia-based thermal barrier coating by interpretable machine learning method4Machine learning-driven optimization of wear-resistant high-manganese steel properties: achieving multi-property matching under specified composition2Accelerating tribological material discovery: A machine learning framework with predictive insights20Machine Learning Enabled Estimation of Formability for Anisotropic Sheet Metals10Machine Learning-Based High Entropy Alloys-Algorithms and Workflow: A Review10Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories33Predicting the creep behavior of thermoset polymers using machine learning techniques0Accelerating the Discovery of Low-Dielectric Polyimides Based on Interpretable Machine Learning7Toward accurate machine learning‐driven prediction of polymeric composites properties based on experimental data13Recent advances in machine learning-assisted design of additive manufacturing metastructures: a review55Materials informatics: A review of AI and machine learning tools, platforms, data repositories, and applications to architectured porous materials41ML-guided prediction of hydrogen hydrate formation: Guiding experiment design and mechanistic insight1From Algorithms to Applications: A Comprehensive Review of Machine Learning in Computational Materials Science5From Research Trend to Performance Prediction: Metaheuristic-Driven Machine Learning Optimization for Cement Pastes Containing Bio-Based Phase Change Materials9Machine learning approach to predict Curie temperature in binary alloys2Recent advances of multicomponent VOCs catalytic oxidation: Insights into mechanism, mixing effect, and catalyst design13Intelligent microstructure materials for diagnosis and treatment of osteoarthritis: progress and AI-enpowered future5Development and Application of Precision Forming Process Database0Democratizing machine learning in chemistry with community-engaged test sets2Machine Learning‐Accelerated Specific Surface Prediction Strategy in Janus‐Based Z‐Scheme Heterostructures for Efficient Photocatalytic Water Splitting30Research on the Variation Law of Resistivity of Composite Insulating Materials under Multiple Physical Fields0Microstructure manipulation and high-temperature service performance enhancement of nickel-based superalloys13Investigating the Growth of GaSb Single Crystals through Optimized LEC Method Utilizing Finite Element Simulation and Machine Learning Techniques0Materials discovery acceleration by using conditional generative methodology7Compositionally-informed machine learning for solid-state electrolyte design: a structure-free approach1Computational Methods for Predicting Material Properties in Industry 4.00Exploration of highly stable and efficient pseudohalide double perovskite doping schemes for solar cells0Strategies to enhance the supercapacitance performance of 2D MXenes through defect engineering, doping, hybridization, and magnetic field assistance: recent progress and challenges10Accelerated prediction of elastic constants in cubic crystals via interpretable machine learning: From metallic alloys to broader material systems1Accelerated Discovery of Graphene Kirigami with an Enhanced Elastocaloric Effect via Machine Learning0Exploring the structural basis of organic compounds by predicting experimental IR peaks: a machine learning analysis1Strengthening mechanism of nano-twinned Cu-Ag polycrystalline alloys: based on molecular dynamics0Enhancing elastic energy focusing in multimode strain regions via Bayesian optimization of gradient-index phononic crystals for energy harvesting0Physics-informed Neural Network-based Prediction of Multi-factor Coupled Thermal-oxidative Aging Behavior in Polyamide66-Glass Fiber Composites3Machine Learning-Enhanced Molecular Dynamics: Current State, Challenges and Perspectives5Shape Memorable and Self‐Healable Smart Hydrogels and Emerging Directions6Towards a sustainable high-quality materials data ecosystem: frameworks and strategies6Material Discovery, Renewable Energy, and the Scope of Shallow Learning and Deep Learning1Machine learning in conductivity development of specific alloys1Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges7Dual‐Atom Catalysts for Synergistic Adsorption and Adjustment of Reaction Kinetics of Polysulfides: Design Strategies, Characterizations and Mechanisms1High-strength and wear-resistant precious metal sliding electrical contact materials1Machine Learning‐Enabled Inverse Design of Martensitic Stainless Steels With Enhanced Hardness and Wear Resistance0Machine learning in sodium-ion battery development: Critical perspectives on design innovation and applications2Machine learning for ionic liquids in CO2 conversion: advances, challenges, and perspectives0Fundamentals to Prospects: Synthetic Tuning of Covalent Organic Frameworks for Solid‐State Hydrogen Storage0Predicting co-segregation in alloys with solute-solute interactions0Molecularly Imprinted Polymeric Sensors for Antibiotic Recognition0Machine learning‐assisted optimization of MoTe 2 neural electrode for enhanced electrochemical performance0Artificial intelligence-assisted design and optimization of electrode materials0Polyimide Membranes for Organic Solvent Nanofiltration ( OSN ): A Comprehensive Review of Crosslinking Strategies, Sustainable Fabrication, and Machine Learning‐Assisted Design0Screening Lithium-Ion Conductors Based on Local and Global Topology0Machine Learning‐Assisted Development in Insulating Materials for High Voltage Applications0

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