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RESEARCH PAPER · Materials Today Proceedings · 2022

Advancement of machine learning in materials science

P. Rajendra, A. Girisha, T. Gunavardhana Naidu

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Advancement of machine learning in materials scienceOriginalOptimizing Predictive Maintenance Decisions: Use of Non-Arbitrary Multi-Covariate Bands in a Novel Condition Assessment under a Machine Learning Approach5Application of artificial intelligence (AI) in the area of corrosion protection22Interpretable Machine Learning Workflow for Evaluating and Analyzing the Performance of High-Entropy GeTe-Based Thermoelectric Materials25Predicting elastic and plastic properties of small iron polycrystals by machine learning10Application of Machine Learning in Material Synthesis and Property Prediction166Nonlinear probabilistic virtual sample generation using Gaussian process latent variable model and fitting for rubber material6Prediction of external corrosion rate in Oil and Gas platforms using ensemble learning8Composition optimization of AlFeCuSiMg alloys based on elastic modules: A combination method of machine learning and molecular dynamics simulation10Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review34Interpretable Machine Learning to Discover Perovskites with High Spontaneous Polarization11Machine learning from a mathematical perspective0Insights into Preparation Methods and Functions of Carbon-Based Solid Acids18Adopting New Machine Learning Approaches on Cox’s Partial Likelihood Parameter Estimation for Predictive Maintenance Decisions8A machine-learning-based composition design of ternary Cu-based Rochow-Müller catalyst with high M2 selectivity5Machine learning and molecular design algorithm assisted discovery of gas separation membranes exceeding the CO2/CH4 and CO2/N2 upper bounds24A brief review of machine learning-assisted Mg alloy design, processing, and property predictions38Different applications of machine learning approaches in materials science and engineering: Comprehensive review68Machine learning and experimental study on a novel Cr–Mo–V–Ti high manganese steel: Microstructure, mechanical properties and abrasive wear behavior12Prediction Model of Material Dynamic Mechanical Properties Embedded with Physical Mechanism Neural Network3Shape memory liquid crystalline polymers: Stimuli‐responsiveness, advanced technologies, and key applications10Accelerated discovery and formation mechanism of high-entropy carbide ceramics using machine learning based on low-cost descriptors18Machine Learning Model to Enhance the Quality of Software Development Risk Management3New Intelligent Computing Techniques for Engineering Materials—A Comprehensive Review0Predicting the Tensile Properties of Automotive Steels at Intermediate Strain Rates via Interpretable Ensemble Machine Learning2Machine Learning-Assisted Multi-Property Prediction and Sintering Mechanism Exploration of Mullite–Corundum Ceramics15Accelerated discovery of near-zero ablation ultra-high temperature ceramics via GAN-enhanced directionally constrained active learning9Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors4Deep learning-based identification of electrodeposition time: a case study using synthetic images of copper coatings2A review of artificial intelligence (AI)-based applications to nanocomposites17Machine learning-based prediction of wear for GH4169 alloy at different temperatures, loads, and displacements4FMEnets: Flow, material, and energy networks for non-ideal plug flow reactor design4Review: Recent progress in titanium alloys development via machine learning and high-throughput preparation4Machine Learning Applications in Composite Material Synthesis and Property Prediction0Synchronized optimization for overall property of steel through integrated modeling2Accelerating Hydrogen Storage Material Discovery: Machine Learning Approach for Predicting Multi-Step Hydrogenation Energetics in Transition Metal-Doped Phosphorene0Accelerating Hydrogen Storage Material Discovery: Machine Learning Approach for Predicting Multi-Step Hydrogenation Energetics in Transition Metal-Doped Phosphorene0Machine learning prediction of multi-step hydrogenation energetics in transition metal-doped phosphorene2Data-driven design of pseudohalide perovskites for optoelectronic applications0Machine Learning‐Enabled Inverse Design of Martensitic Stainless Steels With Enhanced Hardness and Wear Resistance0

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  2. Application of artificial intelligence (AI) in the area of corrosion protection

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  3. Interpretable Machine Learning Workflow for Evaluating and Analyzing the Performance of High-Entropy GeTe-Based Thermoelectric Materials

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    Wenzhao Li, Mingji Liu

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  4. Predicting elastic and plastic properties of small iron polycrystals by machine learning

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  5. Application of Machine Learning in Material Synthesis and Property Prediction

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  6. Nonlinear probabilistic virtual sample generation using Gaussian process latent variable model and fitting for rubber material

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  7. Prediction of external corrosion rate in Oil and Gas platforms using ensemble learning

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  8. Composition optimization of AlFeCuSiMg alloys based on elastic modules: A combination method of machine learning and molecular dynamics simulation

    Materials Today Communications · 2023-11-15

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  9. Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review

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  10. Interpretable Machine Learning to Discover Perovskites with High Spontaneous Polarization

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  11. Machine learning from a mathematical perspective

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  12. Insights into Preparation Methods and Functions of Carbon-Based Solid Acids

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  13. Adopting New Machine Learning Approaches on Cox’s Partial Likelihood Parameter Estimation for Predictive Maintenance Decisions

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  14. A machine-learning-based composition design of ternary Cu-based Rochow-Müller catalyst with high M2 selectivity

    Applied Catalysis A General · 2024-02-07

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  15. Machine learning and molecular design algorithm assisted discovery of gas separation membranes exceeding the CO2/CH4 and CO2/N2 upper bounds

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  16. A brief review of machine learning-assisted Mg alloy design, processing, and property predictions

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  17. Different applications of machine learning approaches in materials science and engineering: Comprehensive review

    Engineering Applications of Artificial Intelligence · 2024-06-20

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  18. Machine learning and experimental study on a novel Cr–Mo–V–Ti high manganese steel: Microstructure, mechanical properties and abrasive wear behavior

    Journal of Materials Research and Technology · 2024-06-24

    Tao Xu, Binguo Fu, Yanfei Jiang, Jinghui Wang, Guolu Li

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  19. Prediction Model of Material Dynamic Mechanical Properties Embedded with Physical Mechanism Neural Network

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    Houchao Wang, Hailong Zhao, Zhenfei Zhan, Hailiang Chen, Minchi Li

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  20. Shape memory liquid crystalline polymers: Stimuli‐responsiveness, advanced technologies, and key applications

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  21. Accelerated discovery and formation mechanism of high-entropy carbide ceramics using machine learning based on low-cost descriptors

    Journal of Alloys and Compounds · 2024-08-09

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  22. Machine Learning Model to Enhance the Quality of Software Development Risk Management

    IEEE International Conference on Control System, Computing and Engineering · 2024-08-23

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  23. New Intelligent Computing Techniques for Engineering Materials—A Comprehensive Review

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  24. Predicting the Tensile Properties of Automotive Steels at Intermediate Strain Rates via Interpretable Ensemble Machine Learning

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  25. Machine Learning-Assisted Multi-Property Prediction and Sintering Mechanism Exploration of Mullite–Corundum Ceramics

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  26. Accelerated discovery of near-zero ablation ultra-high temperature ceramics via GAN-enhanced directionally constrained active learning

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  27. Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors

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  28. Deep learning-based identification of electrodeposition time: a case study using synthetic images of copper coatings

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  29. A review of artificial intelligence (AI)-based applications to nanocomposites

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  30. Machine learning-based prediction of wear for GH4169 alloy at different temperatures, loads, and displacements

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  31. FMEnets: Flow, material, and energy networks for non-ideal plug flow reactor design

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  32. Review: Recent progress in titanium alloys development via machine learning and high-throughput preparation

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  33. Machine Learning Applications in Composite Material Synthesis and Property Prediction

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  34. Synchronized optimization for overall property of steel through integrated modeling

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  35. Accelerating Hydrogen Storage Material Discovery: Machine Learning Approach for Predicting Multi-Step Hydrogenation Energetics in Transition Metal-Doped Phosphorene

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  36. Accelerating Hydrogen Storage Material Discovery: Machine Learning Approach for Predicting Multi-Step Hydrogenation Energetics in Transition Metal-Doped Phosphorene

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  37. Machine learning prediction of multi-step hydrogenation energetics in transition metal-doped phosphorene

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  38. Data-driven design of pseudohalide perovskites for optoelectronic applications

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  39. Machine Learning‐Enabled Inverse Design of Martensitic Stainless Steels With Enhanced Hardness and Wear Resistance

    steel research international · 2026-06-23

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