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RESEARCH PAPER · InfoMat · 2019

Machine learning in materials science

Jing Wei, Xuan Chu, Xiangyu Sun, Kun Xu, Hui‐Xiong Deng, Ji-Gen Chen, Zhongming Wei, Ming Lei

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42 direct citing works

Machine learning in materials scienceOriginalSulfonate-grafted conjugated microporous polymers for fast removal of cationic dyes from water72Highly Robust, Flexible, and Large‐Scale 3D‐Metallized Sponge for High‐Performance Electromagnetic Interference Shielding62Graded Bandgap Perovskite with Intrinsic n–p Homojunction Expands Photon Harvesting Range and Enables All Transport Layer‐Free Perovskite Solar Cells78Artificial Intelligence to Power the Future of Materials Science and Engineering154Computational functionality‐driven design of semiconductors for optoelectronic applications58WSe 2 2D p‐type semiconductor‐based electronic devices for information technology: Design, preparation, and applications259The photovoltaic conversion enhancement of NiO/Tm:CeO2/SnO2 transparent p-n junction device with dual-functional Tm:CeO2 quantum dots75Learning physical properties of liquid crystals with deep convolutional neural networks73Deep learning analysis on microscopic imaging in materials science195Machine learning prediction of self-diffusion in Lennard-Jones fluids54The photocatalytic overall water splitting hydrogen production of g-C3N4/CdS hollow core–shell heterojunction via the HER/OER matching of Pt/MnOx190Machine Learning Lattice Constants for Cubic Perovskite Compounds53Machine learning for halide perovskite materials130Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA63Ternary Co3O4/CdS/SrTiO3 core-shell pn junctions toward enhanced photocatalytic hydrogen production activity46Role of machine learning in the field of Fiber reinforced polymer composites: A preliminary discussion56Deep learning in electron microscopy103Machine learning for molecular thermodynamics65Machine learning for advanced energy materials208A general and transferable deep learning framework for predicting phase formation in materials93Machine learning for material characterization with an application for predicting mechanical properties139Machine Learning for Transition-Metal-Based Hydrogen Generation Electrocatalysts122The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts49Emerging artificial intelligence in piezoelectric and triboelectric nanogenerators155Encoding the atomic structure for machine learning in materials science85Predicting hydrogen storage in MOFs via machine learning181High temperature oxidation of corrosion resistant alloys from machine learning99Is machine learning redefining the perovskite solar cells?59Search for ABO3 Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies65Machine learning and finite element analysis: An integrated approach for fatigue lifetime prediction of adhesively bonded joints47Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries412Droplet based microfluidics integrated with machine learning82Applications of artificial intelligence and machine learning in metal additive manufacturing62First-principles computational insights into lithium battery cathode materials63Novel Materials for Urban Farming91Artificial intelligence: A powerful paradigm for scientific research1702Ensemble-based machine learning models for phase prediction in high entropy alloys49Targeted design of advanced electrocatalysts by machine learning110Uncertainty Prediction for Machine Learning Models of Material Properties95Deep machine learning potentials for multicomponent metallic melts: Development, predictability and compositional transferability58Materials by design at high pressures77Sustainable and efficient technologies for removal and recovery of toxic and valuable metals from wastewater: Recent progress, challenges, and future perspectives192Digital Microscale Electrochemical Energy Storage Devices for a Fully Connected and Intelligent World46A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification113A bright future for engineering piezoelectric 2D crystals119Supervised Machine Learning-Aided SCAPS-Based Quantitative Analysis for the Discovery of Optimum Bromine Doping in Methylammonium Tin-Based Perovskite (MASnI 3– x Br x )53Conductive Polyvinyl Alcohol/Silver Nanoparticles Hydrogel Sensor with Large Draw Ratio, High Sensitivity and High Stability for Human Behavior Monitoring88Membrane-type acoustic metamaterial using cork sheets and attached masses based on reused materials79High‐Efficiency Non‐Fullerene Acceptors Developed by Machine Learning and Quantum Chemistry78Artificial Neuron Networks Enabled Identification and Characterizations of 2D Materials and van der Waals Heterostructures50Material research from the viewpoint of functional motifs103Inverse Design of Materials by Machine Learning137Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials102Application of deep neural network learning in composites design64Current Trends in Fluid Research in the Era of Artificial Intelligence: A Review65Early detection of Alzheimer’s disease based on the state-of-the-art deep learning approach: a comprehensive survey81Inkjet‐Printed rGO/binary Metal Oxide Sensor for Predictive Gas Sensing in a Mixed Environment121Discovery and prediction capabilities in metal-based nanomaterials: An overview of the application of machine learning techniques and some recent advances47Recent 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-art70Data‐Driven Materials Innovation and Applications187Machine Learning Approach to Delineate the Impact of Material Properties on Solar Cell Device Physics74Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review50A Generative Approach to Materials Discovery, Design, and Optimization121Artificial Intelligence, Machine Learning, and Deep Learning in Structural Engineering: A Scientometrics Review of Trends and Best Practices317Machine learning for microalgae detection and utilization82A State-of-the-Art Review on Machine Learning-Based Multiscale Modeling, Simulation, Homogenization and Design of Materials222A 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 limitations51Factors affecting the growth formation of nanostructures and their impact on electrode materials: A systematic review58Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics141Machine learning accelerates the materials discovery142Benchmarking AutoML for regression tasks on small tabular data in materials design48A flexible semidry electrode for long-term, high-quality electrocardiogram monitoring65Rapid, autonomous high-throughput characterization of hydrogel rheological properties via automated sensing and physics-guided machine learning45Predict the phase formation of high-entropy alloys by compositions101A review on the application of molecular descriptors and machine learning in polymer design108Functions and applications of artificial intelligence in droplet microfluidics49Interpretable models for extrapolation in scientific machine learning98Steering self-organisation through confinement80Enhancing property prediction and process optimization in building materials through machine learning: A review161Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170Understanding and Designing a High-Performance Ultrafiltration Membrane Using Machine Learning124Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures73Generative Models as an Emerging Paradigm in the Chemical Sciences313Flame retardant properties of metal hydroxide-based polymer composites: A machine learning approach45Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives241Machine learning for perovskite solar cell design58Machine learning of spectra-property relationship for imperfect and small chemistry data46Prediction of mechanical properties of composite materials using multimodal fusion learning64Interpretable Catalysis Models Using Machine Learning with Spectroscopic Descriptors71Challenges and opportunities in low-dimensional thermoelectric nanomaterials109Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles65Machine learning-assisted crystal engineering of a zeolite58Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0103Methods, progresses, and opportunities of materials informatics72AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)122Comparative study on the performance of different machine learning techniques to predict the shear strength of RC deep beams: Model selection and industry implications48A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis62Applications of machine learning in supercritical fluids research47Machine learning in analytical chemistry: From synthesis of nanostructures to their applications in luminescence sensing75StressD: 2D Stress estimation using denoising diffusion model54Application of Machine Learning in Material Synthesis and Property Prediction166Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology78TRACING THE EVOLUTION OF AI AND MACHINE LEARNING APPLICATIONS IN ADVANCING MATERIALS DISCOVERY AND PRODUCTION PROCESSES53AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials122A machine learning approach for accelerated design of magnesium alloys. Part A: Alloy data and property space57Review on applications of artificial neural networks to develop high entropy alloys: A state-of-the-art technique47Advancements in Microwave Absorption Motivated by Interdisciplinary Research310Exploring the Potential of Artificial Intelligence for Hydrogel Development—A Short Review126Recent advances in synthesis of metal-organic frameworks (MOFs)-derived metal oxides and its composites for electrochemical energy storage applications73Advances of machine learning in materials science: Ideas and techniques105Scope of machine learning in materials research—A review230A Deep Learning Approach for Crop Disease and Pest Classification Using Swin Transformer and Dual-Attention Multi-Scale Fusion Network57Development and application of Few-shot learning methods in materials science under data scarcity47The DFT and Machine Learning Method Accelerated the Discovery of DMSCs with High ORR and OER Catalytic Activities114Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future69Advances and challenges in high-performance cathodes for protonic solid oxide fuel cells and machine learning-guided perspectives57AI methods in materials design, discovery and manufacturing: A review142Advancing 3D bioprinting through machine learning and artificial intelligence96AI-based additive manufacturing for future food: Potential applications, challenges and possible solutions62Enhanced Sampling with Machine Learning133Multiscale Thermodynamics-Informed Neural Networks (MuTINN) towards fast and frugal inelastic computation of woven composite structures51Machine Learning Aided Design and Optimization of Thermal Metamaterials195Machine learning for CO 2 capture and conversion: A review60Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers65Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics52Half-Heusler thermoelectrics: Advances from materials fundamental to device engineering142In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back78Machine learning to explore high-entropy alloys with desired enthalpy for room-temperature hydrogen storage: Prediction of density functional theory and experimental data96Different applications of machine learning approaches in materials science and engineering: Comprehensive review68Advanced Computational Methods for Modeling, Prediction and Optimization—A Review132Applications of artificial intelligence/machine learning to high-performance composites159Mechanical energy harvesting based on the piezoelectric materials: Recent advances and future perspectives137Computational and Machine Learning Methods for CO 2 Capture Using Metal–Organic Frameworks74Catalysis in the digital age: Unlocking the power of data with machine learning75From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks91Application of artificial intelligence in the materials science, with a special focus on fuel cells and electrolyzers78Optoelectronic performance prediction of HgCdTe homojunction photodetector in long wave infrared spectral region using traditional simulations and machine learning models66Reticular Materials for Photocatalysis77Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials188Advancing frontiers in CO2 capture: The renaissance of biomass-derived carbon materials62Recent advances in perovskite air electrode materials for protonic solid oxide electrochemical cells52Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm46Advancing perovskite photovoltaic technology through machine learning‐driven automation49Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques47Identifying MOFs for electrochemical energy storage via density functional theory and machine learning51Biomimetic Intelligent Thermal Management Materials: From Nature‐Inspired Design to Machine‐Learning‐Driven Discovery50Recent Progress of Artificial Intelligence Application in Polymer Materials54Physics-informed machine learning meets renewable energy systems: A review of advances, challenges, guidelines, and future outlooks53

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  1. Sulfonate-grafted conjugated microporous polymers for fast removal of cationic dyes from water

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  2. Investigations on p- and n-type diluted magnetic semiconductors X/Mn-codoped LiZnN (X= Li, Na and K)

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  3. Highly Robust, Flexible, and Large‐Scale 3D‐Metallized Sponge for High‐Performance Electromagnetic Interference Shielding

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  4. The enhanced photocatalytic hydrogen production of the non-noble metal co-catalyst Mo2C/CdS hollow core-shell composite with CdMoO4 transition layer

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  8. Graded Bandgap Perovskite with Intrinsic n–p Homojunction Expands Photon Harvesting Range and Enables All Transport Layer‐Free Perovskite Solar Cells

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  10. Artificial Intelligence to Power the Future of Materials Science and Engineering

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  13. WSe 2 2D p‐type semiconductor‐based electronic devices for information technology: Design, preparation, and applications

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  15. Effect of Li interstitials on magnetic properties of V-doped LiZnAs

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  16. The photovoltaic conversion enhancement of NiO/Tm:CeO2/SnO2 transparent p-n junction device with dual-functional Tm:CeO2 quantum dots

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  36. Structural features modeling of substituted hydroxyapatite nanopowders as bone fillers via machine learning

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  37. Modulating the percolation network of polymer nanocomposites for flexible sensors

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  38. Ternary Co3O4/CdS/SrTiO3 core-shell pn junctions toward enhanced photocatalytic hydrogen production activity

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  39. Role of machine learning in the field of Fiber reinforced polymer composites: A preliminary discussion

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  45. The CoSx-modified g-C3N4 nanosheets towards photocatalytic water splitting hydrogen production enhancement

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  46. Transfer Learned Designer Polymers For Organic Solar Cells

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  47. Machine learning for molecular thermodynamics

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  48. Machine learning for advanced energy materials

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  49. A general and transferable deep learning framework for predicting phase formation in materials

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  50. A Proposed Approach for Production in ERP Systems Using Support Vector Machine Algorithm

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