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RESEARCH PAPER · Chinese Physics B · 2020

Machine learning in materials design: Algorithm and application*

Zhilong Song, Xiwen Chen, Fanbin Meng, Guanjian Cheng, Chen Wang, Zhongti Sun, Wan‐Jian Yin

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Machine learning in materials design: Algorithm and application*OriginalEfficient sampling for decision making in materials discovery*10Machine Learning in Magnetic Materials48Machine Learning of Quasiparticle Energies in Molecules and Clusters17Novel Materials for Urban Farming91A universal neural network for learning phases13Going Beyond the d‐Band Center to Describe CO2 Activation on Single‐Atom Alloys32Accelerated prediction of Cu-based single-atom alloy catalysts for CO2 reduction by machine learning77Mechanistic Data Science for Modeling and Design of Aerospace Composite Materials1Accurate Prediction of the Boiling Point of Organic Molecules by Multi-Component Heterogeneous Learning Model6Modelling of functional properties of shape-memory alloys by machine learning methods4A machine learning model for screening thermodynamic stable lead-free halide double perovskites44Perspective: Predicting and optimizing thermal transport properties with machine learning methods54Data-Driven Construction Method of Material Mechanical Behavior Model3A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys33Digital advancements in smart materials design and multifunctional coating manufacturing46Predicting the Textural Properties of Plant-Based Meat Analogs with Machine Learning37Latest Advances in Manufacturing and Machine Learning of Bulk Metallic Glasses14Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems112Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170Predicting thermodynamic stability of magnesium alloys in machine learning33Adaptive Design of Alloys for CO 2 Activation and Methanation via Reinforcement Learning Monte Carlo Tree Search Algorithm14Advances in machine learning- and artificial intelligence-assisted material design of steels109Machine Learning Accelerated Study of Defect Energy Levels in Perovskites21Machine learning predictions of high-Curie-temperature materials19Graph neural networks with molecular segmentation for property prediction and structure–property relationship discovery17Properties prediction of composites based on machine learning models: A focus on statistical index approaches51Machine Learning Aided Design and Optimization of Thermal Metamaterials195Influence of feature-to-feature interactions on chloride migration in type-I cement concrete: A robust modeling approach using extra random forest10Machine learning-based crack prediction modeling of laser cladding coatings2A review on machine learning-guided design of energy materials19Machine-learning synergy in high-entropy alloys: A review43Deep Learning and Machine Learning for Materials Design3Machine learning in advancing anode materials for Lithium-Ion batteries – A review17Machine learning-based constitutive modelling for material non-linearity: A review65Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks9Robust extreme gradient boosting model for predicting the behavior of RC slabs under impact loading: key influencing factors and performance insights0Bibliometric analysis of artificial intelligence in high-to-room temperature superconductors: insights from the Scopus database2High‐throughput calculation integrated with stacking ensemble machine learning for predicting elastic properties of refractory multi‐principal element alloys8Utilizing big data and categorical boosting modeling methodology to interpret the load-deflection behavior of steel fiber-reinforced concrete beams7Recent advances in machine learning guided mechanical properties prediction and design of two-dimensional materials16Intelligent screening model for uranium alloy corrosion substitute alloys based on machine learning2Enhancing design and behavioral understanding of steel fiber-reinforced concrete flat slabs through a robust machine learning framework4Integrating Materials Representations Into Feature Engineering in Machine Learning for Crystalline Materials: From Local to Global Chemistry‐Structure Information Coupling5Robust surrogate modeling for glass forming process0Optimizing the durability of cement-based systems through data-driven modeling and sensitivity analysis of chloride migration coefficients0Differential Evolution-Enhanced Descriptor Selection for Low-Alloy Steel Performance Prediction0Machine Learning‐Driven Perovskite Research from Experimental Exploration to Industrial Development6Engineering ZnS nanoarchitectures: Defects and dynamic surfaces for improved photocatalysis7Accelerating the discovery and optimization of metal-organic framework materials via machine learning64Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review50The Role of Artificial Intelligence in Biomaterials Science: A Review21Machine-learning-driven synergistic optimization of multi-objective mechanical properties in steel materials2Machine learning in nanoscience and beyond: Workflows, data processing, XAI and ITAP metrics, language-based models5A Comprehensive Review of Composite Materials: Current and Future Prospects in Aerospace Applications30Tailor-made adsorbent materials on machine learning instructions for efficient purification of heavy metal contaminants in wastewater1Bayesian-optimized machine learning framework for predicting Thermal Contact Resistance2Machine learning insights into microstructural origins of transport and mechanical properties in porous microstructures2Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges7Integrating NSGA-II and TOPSIS for Stacking Model Optimization in Pursuit of Halide Double Perovskite Screening0Artificial Intelligence in Surface Coating Processes on Inconel Superalloy and Other Materials0Metal matrix composites: Bioinspired architectures, reinforcements and interfaces, multifunctionality, and data‑driven intelligent manufacturing0

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  4. Novel Materials for Urban Farming

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  8. Mechanistic Data Science for Modeling and Design of Aerospace Composite Materials

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  19. Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

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  20. Predicting thermodynamic stability of magnesium alloys in machine learning

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  21. Adaptive Design of Alloys for CO 2 Activation and Methanation via Reinforcement Learning Monte Carlo Tree Search Algorithm

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  22. Advances in machine learning- and artificial intelligence-assisted material design of steels

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  23. Machine Learning Accelerated Study of Defect Energy Levels in Perovskites

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