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RESEARCH PAPER · Materials Today Communications · 2023

A machine learning-driven framework for the property prediction and generative design of multiple principal element alloys

Zhe Li, S. Li, N. Birbilis

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A machine learning-driven framework for the property prediction and generative design of multiple principal element alloysOriginalA Conceptual Theory of Measurement Validity for AI-Generated Materials Properties1Interpretability in Materials AI — What “Explanation” Means and How It Should Be Evaluated Conceptually0Inverse Design of Aluminium Alloys Using Genetic Algorithm: A Class-Based Workflow18Multi-objective Optimization-Oriented Generative Adversarial Design for Multi-principal Element Alloys13Current Progress in Aqueous Corrosion of Multi-Principal Element Alloys22A machine learning method based on TPE-XGBoost model for TRIP/TWIP near-β titanium alloy design11RETRACTED: Exploring high entropy alloys: A review on thermodynamic design and computational modeling strategies for advanced materials applications95Machine Learning Prediction of the Corrosion Rate of Zinc-Based Alloys Containing Copper, Lithium, Magnesium, and Silver17State-of-the-art review on various applications of machine learning techniques in materials science and engineering26Machine Learning for the Design of High Entropy Alloys: A Review3Regional Explanations and Diverse Molecular Representations in Cheminformatics: A Comparative Study4Promising Multicomponent Cubic Alloys for Hydrogen Storage: Insights from First-Principles Calculations and Machine Learning10Exploration for the physical origin and impact of chemical short-range order in high-entropy alloys: Machine learning-assisted study9Experimentally validated inverse design of FeNiCrCoCu MPEAs and unlocking key insights with explainable AI11Machine learning-driven alloy digital design for hydrogen storage: a review12An interpretable stacking ensemble model for high-entropy alloy mechanical property prediction11Computation- and process-based design for advanced structural high-entropy alloy development and analyses: A critical review32Advances in machine learning for predicting fatigue behavior: from material properties to fatigue life and fatigue crack growth21Generating and leveraging explanations of AI/ML models in materials and manufacturing research13Study on Hardness Prediction and Design of Ti-Zr-Nb-Ta Refractory Alloy Based on Interpretable Machine Learning and Generative Model0A physics-informed machine learning framework for accelerated discovery of single-phase B2 multi-principal element intermetallics17End-to-end prediction and design of additively manufacturable alloys using a generative AlloyGPT model22A survey of AI-supported materials informatics7RF-NSGA-II framework for inverse design of high-performance Mg-Gd-based magnesium alloys2Predicting and designing properties of twelve alloy families using artificial neural networks and generative adversarial networks1Data-driven design of Cantor-type FeNiCoCrMn alloys: integrating physics-based models, explainable AI, and experiment0Machine learning-driven closed-loop discovery of hard multiple principal element alloys2Generative Design for Alloys: Harnessing Generative Models for Faster Discovery6An Integrated NLP‐ML Framework for Property Prediction and Design of Steels0A Review of Knowledge Base Construction Strategies for LLM-based Intelligent Decision Support System in Material Selection and Process Planning0Multimodal Learning with Rashomon Analysis for Battery Discharge Capacity Prediction0Machine Learning‐Assisted Design of the Al–Co–Cr–Fe–Ni–Ti‐Based High‐Entropy Alloys With Target Room‐Temperature Tensile Elongation0Challenges in modeling solid–solid phase transformation kinetics0

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  1. A Conceptual Theory of Measurement Validity for AI-Generated Materials Properties

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  2. Interpretability in Materials AI — What “Explanation” Means and How It Should Be Evaluated Conceptually

    Journal of Artificial Intelligence for Materials Science · 2024-01-01

    Maria Hernandez, Carlos Vega, Lucia Torres

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  3. Inverse Design of Aluminium Alloys Using Genetic Algorithm: A Class-Based Workflow

    Metals · 2024-02-16

    Ninad Bhat, Amanda Susan Barnard, N. Birbilis

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  4. Multi-objective Optimization-Oriented Generative Adversarial Design for Multi-principal Element Alloys

    Integrating materials and manufacturing innovation · 2024-04-30

    Ziyuan Li, N. Birbilis

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  5. Current Progress in Aqueous Corrosion of Multi-Principal Element Alloys

    Metallurgical and Materials Transactions A · 2024-06-15

    M. Ghorbani, Ziyuan Li, Yao Qiu, Philippe Marcus, John R. Scully, Oumaïma Gharbi, Hong Luo, Rajeev Gupta, Zhuoran Zeng, Hamish L. Fraser, Mitra L. Taheri, N. Birbilis

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  6. A machine learning method based on TPE-XGBoost model for TRIP/TWIP near-β titanium alloy design

    Materials Today Communications · 2024-09-04

    Zheng Wang, Haoyu Zhang, Shengyuan Wang, Jun Jie Cheng, Chuan Wang, Ge Zhou, Siqian Zhang, Lijia Chen

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  7. RETRACTED: Exploring high entropy alloys: A review on thermodynamic design and computational modeling strategies for advanced materials applications

    Heliyon · 2024-10-30

    Peter Ifeolu Odetola, Bukola Joseph Babalola, Ayodeji Ebenezer Afolabi, Ufoma Silas Anamu, Emmanuel Olorundaisi, Mutombo Christian Umba, Thabang A. Phahlane, Olusoji Oluremi Ayodele, Peter Apata Olubambi

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  8. Machine Learning Prediction of the Corrosion Rate of Zinc-Based Alloys Containing Copper, Lithium, Magnesium, and Silver

    The Journal of Physical Chemistry Letters · 2024-12-20

    Artur R. Davletshin, Elena Aleksandrovna Korznikova, Andrey A. Kistanov

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  9. State-of-the-art review on various applications of machine learning techniques in materials science and engineering

    Chemical Engineering Science · 2024-12-24

    Bing Yu, LiQin Zhang, Xiaoxia Ye, JunQi Wu, JunQi Wu, HuaYong Ying, Wei Zhu, Xiaoming Wu, Xiaoming Wu

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  10. Machine Learning for the Design of High Entropy Alloys: A Review

    World Scientific Annual Review of Functional Materials · 2025-01-01

    Si Rong Ng, Verner Soh, Zhongji Sun, Pei Wang, Chee Koon Ng

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  11. Regional Explanations and Diverse Molecular Representations in Cheminformatics: A Comparative Study

    Intelligent Computing · 2025-01-01

    Xin Wang, Amanda Susan Barnard, Sichao Li

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  12. Promising Multicomponent Cubic Alloys for Hydrogen Storage: Insights from First-Principles Calculations and Machine Learning

    ACS Applied Energy Materials · 2025-03-04

    Zeineb Nefzi, Céline Barreteau, Lucas Levrel, Jean‐Claude Crivello

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  13. Exploration for the physical origin and impact of chemical short-range order in high-entropy alloys: Machine learning-assisted study

    Materials & Design · 2025-03-31

    Panhua Shi, Zhen Xie, Jiaxuan Si, Jianqiao Yu, Xiaoyong Wu, Yaojun Li, Qiu Yu Xu, Yuexia Wang

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  14. Experimentally validated inverse design of FeNiCrCoCu MPEAs and unlocking key insights with explainable AI

    npj Computational Materials · 2025-05-15

    Fangxi Wang, Allana G. Iwanicki, Abhishek T. Sose, Lucas A. Pressley, Tyrel M. McQueen, Sanket A. Deshmukh

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  15. Machine learning-driven alloy digital design for hydrogen storage: a review

    Digital Twin · 2025-06-05

    Tianqi Liu, Lufeng Xue, Bo Ci Cheng, Yu Zhao, Bang Dou, Marcelo Paredes, Lu-Kai Song

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  16. An interpretable stacking ensemble model for high-entropy alloy mechanical property prediction

    Frontiers in Materials · 2025-06-18

    Songpeng Zhao, Zeyuan Li, Changshuai Yin, Zhaofu Zhang, Teng Long, Jingjing Yang, Ruyue Cao, Yuzheng Guo

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  17. Computation- and process-based design for advanced structural high-entropy alloy development and analyses: A critical review

    Progress in Materials Science · 2025-07-15

    Zaigham Saeed Toor, Renhao Wu, Muhammad Raihan Hashmi, Jeong Ah Lee, Xiaoqing Li, Harada Yuji, Haiming Zhang, Hyoung Seop Kim

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  18. Advances in machine learning for predicting fatigue behavior: from material properties to fatigue life and fatigue crack growth

    International Journal of Structural Integrity · 2025-08-05

    Zhixin Zhan, Mengwei Zhang, Xiaofan He, Xiongfei Li, Zihui Wang, Xulong Chen, Bo Han, Weiping Hu, Qingchun Meng, Hua Long Li

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  19. Generating and leveraging explanations of AI/ML models in materials and manufacturing research

    Patterns · 2025-08-11

    Erick J. Braham, Jennifer M. Ruddock, James O. Hardin

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  20. Study on Hardness Prediction and Design of Ti-Zr-Nb-Ta Refractory Alloy Based on Interpretable Machine Learning and Generative Model

    JOM · 2025-08-11

    Chengcheng Liu, Hang Su

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  21. A physics-informed machine learning framework for accelerated discovery of single-phase B2 multi-principal element intermetallics

    npj Computational Materials · 2025-08-27

    Weijiang Zhao, Zhaoqi Chen, Yinghui Shang, Qing Wang, Li Wang, Bin Liu, Yong Liu, Yong Gang Yang

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  22. End-to-end prediction and design of additively manufacturable alloys using a generative AlloyGPT model

    npj Computational Materials · 2025-09-26

    Bo Ni, Benjamin Glaser, Seyedeh Mohadeseh Taheri-Mousavi

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  23. A survey of AI-supported materials informatics

    Computer Science Review · 2025-10-17

    Sanjay Chakraborty, Jonas Björk, Martin Dahlqvist, Johanna Rosen, Fredrik Heintz

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  24. RF-NSGA-II framework for inverse design of high-performance Mg-Gd-based magnesium alloys

    Journal of Materials Informatics · 2025-11-14

    Yunchuan Cheng, Lei Wang, Zhihua Dong, Zengyong Zheng, Zhihong Xia, Shengwen Bai, Jiangfeng Song, Bin Jiang

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  25. Predicting and designing properties of twelve alloy families using artificial neural networks and generative adversarial networks

    Bulletin of Materials Science · 2025-12-20

    O Borgard, Natthaphol Chomsaeng, Komkrisd Wongtimnoi, Laurent Mezeix

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  26. Data-driven design of Cantor-type FeNiCoCrMn alloys: integrating physics-based models, explainable AI, and experiment

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    Fangxi Wang, Allana G. Iwanicki, Abhishek T. Sose, Lucas A. Pressley, Tyrel M. McQueen, Sanket A. Deshmukh

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  27. Machine learning-driven closed-loop discovery of hard multiple principal element alloys

    Materials Horizons · 2026-01-01

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  28. Generative Design for Alloys: Harnessing Generative Models for Faster Discovery

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  29. An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

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  30. A Review of Knowledge Base Construction Strategies for LLM-based Intelligent Decision Support System in Material Selection and Process Planning

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  31. Multimodal Learning with Rashomon Analysis for Battery Discharge Capacity Prediction

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  32. Machine Learning‐Assisted Design of the Al–Co–Cr–Fe–Ni–Ti‐Based High‐Entropy Alloys With Target Room‐Temperature Tensile Elongation

    Advanced Engineering Materials · 2026-09-03

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  33. Challenges in modeling solid–solid phase transformation kinetics

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