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RESEARCH PAPER · Journal of Material Science and Technology · 2020

Machine learning in materials genome initiative: A review

Yingli Liu, Chen Xu Niu, Zhuo Wang, Yong Gan, Yan Hong Zhu, Shuhong Sun, Tao Shen

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Machine learning in materials genome initiative: A reviewOriginalOptimization of Machine Learning in Predicting Fracture Behaviour of Materials in AI0Electrocatalyst design for aprotic Li–CO 2 batteries115The partition principles for atomic-scale structures and their physical properties: application to the nonlinear optical crystal material KBe 2 BO 3 F 210Research on the Construction of the General Control Platform for Yunnan Rare and Precious Metal Materials Genetic Engineering2High-throughput development and applications of the compositional mechanical property map of the β titanium alloys51Designing Al-Zn-Mg-Cu Alloys Using Machine Learning1Digital-intellectual design of microporous organic polymers15Simulation and Modelling of Polymers2Thermal Nanostructure Design by Materials Informatics0Text mining of hypereutectic Al-Si alloys literature based on active learning9Data-driven reduced homogenization for transient diffusion problems with emergent history effects22Machine learning for design, phase transformation and mechanical properties of alloys121Big data and machine learning for materials science180Processing Technique Selection for Steels Based on Mechanical Properties Using Machine Learning Framework2A two-step fused machine learning approach for the prediction of glass-forming ability of metallic glasses34NNI‐SMOTE‐XGBoost: A Novel Small Sample Analysis Method for Properties Prediction of Polymer Materials32A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications76Materials Representation and Transfer Learning for Multi-Property Prediction4Materials Representation and Transfer Learning for Multi-Property Prediction1Materials representation and transfer learning for multi-property prediction71Machine learning in predicting mechanical behavior of additively manufactured parts200A Supervised Learning Neural Network Approach for the Prediction of Supercapacitive Energy Storage Materials5Search for ABO3 Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies65Improved Al-Mg alloy surface segregation predictions with a machine learning atomistic potential39A comparative study and development of a novel deep learning architecture for accelerated identification of microstructure in materials science20Using multiple regression analysis to predict directionally solidified TiAl mechanical property24A hybrid machine learning model for predicting continuous cooling transformation diagrams in welding heat-affected zone of low alloy steels53Quantum computing enhanced machine learning for physico-chemical applications2Accelerated design for magnetic high entropy alloys using data-driven multi-objective optimization34Defining inkjet printing conditions of superconducting cuprate films through machine learning14Design of high strength and electrically conductive aluminium alloys by machine learning14In Situ Experiments: Paving Ways for Rapid Development of Structural Metallic Materials for a Sustainable Future2Quantum machine learning for chemistry and physics151Supplementary Data of ‘Discovering Novel Low Young's Modulus Nb–Ti–Zr Biomedical Alloys Via Combined Machine Learning and First Principles Approach’0Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers1Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning32Machine learning-enabled prediction of density and defects in additively manufactured Inconel 718 alloy25Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design285Mapping the creep life of nickel-based SX superalloys in a large compositional space by a two-model linkage machine learning method31Machine learning approaches for prediction of properties of natural fiber composites : Apriori algorithm18New Opportunity: Machine Learning for Polymer Materials Design and Discovery127Machine Learning in Materials Chemistry: An Invitation46Measuring thermal conductivity of materials at room temperature in atmosphere by using a continuous-wave laser and neural network model11Deep learning modeling strategy for material science: from natural materials to metamaterials29Inverse Design of Materials by Machine Learning137Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations52The living interface between synthetic biology and biomaterial design218Recent advances and applications of deep learning methods in materials science1161Investigation of surface roughness and material removal rate of WEDM of SS304 using ANOVA and regression models26A Machine Learning Framework to Predict the Tensile Stress of Natural Rubber: Based on Molecular Dynamics Simulation Data24Bi-objective optimization of thermal conductivity and thermal stress of UO2–Mo–Nb composite through Gaussian process regression and NSGA-II method4Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break15Prediction of Phase Diagrams and Associated Phase Structural Properties19Recent progress in nanocrystalline Sm–Co based magnets35Machine learning approach for ball milling of alumina ceramics0Exploration of the oxidation and ablation resistance of ultra-high-temperature ceramic coatings using machine learning36Predicting Material Properties by Deep Graph Networks7Machine learning driven rationally design of amorphous alloy with improved elastic models34Data-Driven Construction Method of Material Mechanical Behavior Model3Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning91Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art113CRUX: Crowdsourced Materials Science Resource and Workflow Exploration3A machine learning approach for ball milling of alumina ceramics6Recent developments in the application of machine-learning towards accelerated predictive multiscale design and additive manufacturing133Predicting Enthalpy of Formation of Energetic Compounds by Machine Learning: Comparison of Featurization Methods and Algorithms7Machine learning assisted design of aluminum-lithium alloy with high specific modulus and specific strength56Recent Advance of Machine Learning in Selecting New Materials7Self-Supervised Representation Learning of Microstructures from Electron Microscopy for Property Prediction0An improved composition design method for high-performance copper alloys based on various machine learning models11Application of machine learning to reveal relationship between processing-structure-property for polypropylene injection molding38Machine Learning Applications for 3D-Printed Polymers and Their Composites6Computational insight into stability-enhanced systems of anthocyanin with protein/peptide30Discovery of novel low modulus Nb–Ti–Zr biomedical alloys via combined machine learning and first principles approach23Exploring the relationship between lattice distortion and phase stability in a multi-principal element alloy system based on machine learning method34“Lubrication Brain” ― A machine learning framework of lubrication oil molecule design22Knowledge-aware design of high-strength aviation aluminum alloys via machine learning52Creating 3D Objects with Integrated Electronics via Multiphoton Fabrication In Vitro and In Vivo20A Connected World: System-Level Support Through Biosensors15Advances in machine learning- and artificial intelligence-assisted material design of steels109Improved material descriptors for bulk modulus in intermetallic compounds via machine learning27Auto-MatRegressor: liberating machine learning alchemists63Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning63Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel16Multi-objective optimization for high-performance Fe-based metallic glasses via machine learning approach15Artificial Intelligence in Material Genomics2Materials genome strategy for metallic glasses19Center-environment deep transfer machine learning across crystal structures: from spinel oxides to perovskite oxides60Application of Machine Learning in Material Synthesis and Property Prediction166Prospective on methods of design of experiments for limited data scenarios in materials design and engineering6Machine learning-based multi-objective parameter optimization for indium electrorefining8Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization29Layered double hydroxides: next promising materials for energy storage and conversion47Machine Learning-Aided High-Throughput First-Principles Calculations to Predict the Formation Energy of μ Phase12From prediction to design: Recent advances in machine learning for the study of 2D materials90Irradiated mechanical properties predicted by a machine learning method with the Fourier-transform-based feature extension0Review on applications of artificial neural networks to develop high entropy alloys: A state-of-the-art technique47A Comprehensive Review on Combinatorial Film via High-Throughput Techniques22Application of Digital Methods in Polymer Science and Engineering28Facilitating polymer property prediction with machine learning and group interaction modelling methods28Bayesian inference of composition-dependent phase diagrams7Data‐driven mapping‐relationship mining between hardness and mechanical properties of dual‐phase titanium alloys via random forest and statistical analysis7A machine learning approach for the prediction of tensile deformation behavior in wire arc additive manufacturing23Fully Automatized Optimization of Ring-Opening Reactions in Lactone Derivatives via Two-Step Machine Learning7Predicting Natural Rubber Crystallinity by a Novel Machine Learning Algorithm Based on Molecular Dynamics Simulation Data19Modeling for free dendrite growth based on physically-informed machine learning method16Unveiling the crystallization mechanism of cadmium selenide via molecular dynamics simulation with machine-learning-based deep potential20Intelligent design and synthesis of energy catalytic materials25Machine learning – informed development of high entropy alloys with enhanced corrosion resistance35A comparative overview of the electrochemical valorization and incorporation of CO 2 in industrially relevant compounds9Customized design of amorphous solids by generative deep learning6Development and application of Few-shot learning methods in materials science under data scarcity47Integrated hybrid modeling and SHAP (SHapley Additive exPlanations) to predict and explain the adsorption properties of thermoplastic polyurethane (TPU) porous materials28Progress of machine learning in materials design for Li-Ion battery19Reproducibility in materials informatics: lessons from ‘A general-purpose machine learning framework for predicting properties of inorganic materials’26ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT36Accelerated discovery and mapping of block copolymer phase diagrams14Machine learning for polyphenol-based materials10Exploration of high-temperature oxidation resistance laws in ultra-high temperature boride ceramics through data-driven approaches17Materials descriptors of machine learning to boost development of lithium-ion batteries32Reviewing energy efficiency and environmental consciousness in the minerals industry Amidst digital transition: A comprehensive review25Machine learning-based performance predictions for steels considering manufacturing process parameters: a review26Multiscale Computational and Artificial Intelligence Models of Linear and Nonlinear Composites: A Review70Hardware for Deep Learning Acceleration23Advanced ceramics with integrated structures and functions: Machine learning prediction and experimental verification20Overview: Machine Learning for Segmentation and Classification of Complex Steel Microstructures35Simulation‐Guided Design of Gradient Multilayer Microwave Absorber with Tailored Absorption Performance10High-throughput and data-driven machine learning techniques for discovering high-entropy alloys77Rigid structural battery: Progress and outlook13Inversely optimized design of Al-Mg-Si alloys using machine learning methods26Machine learning regression model for predicting the band gap of multi-elements nonlinear optical crystals17Analysis and regularity of ablation resistance performance of ultra-high temperature ceramic matrix composites using data-driven strategy13Exploring the Core-shell Structure of BaTiO3-based Dielectric Ceramics Using Machine Learning Models and Interpretability Analysis3Machine Learning Strategies for Forecasting Discharge Capacity in Lithium-Ion Batteries with NCM Layered Cathode Material1Data-driven atmospheric corrosion prediction model for alloys based on a two-stage machine learning approach35Speeding up the development of solid state electrolyte by machine learning22Review on Some Important Research Progresses in Biodegradable Plastics/Polymers15AI for dielectric capacitors16Research advances in multi-scale numerical simulations of forming and microstructures for magnesium alloys32Polymers simulation using machine learning interatomic potentials19Fault‐tolerant quantum chemical calculations with improved machine‐learning models1Review of progress in calculation and simulation of high-temperature oxidation181Rapid Discovery of Gas Response in Materials Via Density Functional Theory and Machine Learning24Machine Learning Approaches for Designing Electrode Materials for Lithium‐Ion Batteries2Predicting surface roughness of carbon/phenolic composites in extreme environments using machine learning5The interpretable descriptors for fatigue performance of wrought aluminum alloys15Machine learning guided prediction of dynamic energy release in high-entropy alloys17Machine-learning synergy in high-entropy alloys: A review43Database of ternary amorphous alloys based on machine learning1A study of the role of data and model uncertainty in active learning7Wear Resistance Design of Laser Cladding Ni-Based Self-Fluxing Alloy Coating Using Machine Learning14

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