Papers citing Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses
Data-driven machine learning prediction of glass transition temperature and the glass-forming ability of metallic glasses
Nanoscale · 2023-01-01
Jingzi Zhang, Mengkun Zhao, Chengquan Zhong, Jiakai Liu, Kailong Hu, Xi Lin
Read original paper ↗ ·An ensemble learning based amorphous state predictor for multicomponent alloys
Journal of Non-Crystalline Solids · 2023-01-13
Jingyi Hu, Xiang Dong Xu, Yongcheng Cui, Mingxian Xu, Xiaojin Gao, Xiaomei Ji
Read original paper ↗ ·A generative deep learning framework for inverse design of compositionally complex bulk metallic glasses
npj Computational Materials · 2023-01-23
Ziqing Zhou, Yinghui Shang, Xiaodi Liu, Yong Gang Yang
Read original paper ↗ ·Latest Advances in Manufacturing and Machine Learning of Bulk Metallic Glasses
Advanced Engineering Materials · 2023-02-01
Olivia A. Graeve, Mireya S. García-Vázquez, Alejandro Álvaro Ramírez‐Acosta, Zachary Cadieux
Read original paper ↗ ·Overcoming the challenge of the data imbalance for prediction of the glass forming ability in bulk metallic glasses
Materials Today Communications · 2023-02-12
Tao Long, Zhilin Long, Zhilin Long, Bo Pang, Zhuang Li, Xiaowei Liu
Read original paper ↗ ·Chemical composition analysis of ancient glass products based on decision tree
Highlights in Science Engineering and Technology · 2023-04-07
Zhihao He, Weiduo Qin, Changping Duan
Read original paper ↗ ·Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties
Metals · 2023-04-21
Bulat N. Galimzyanov, Maria A. Doronina, Anatolii Vasilievich Mokshin
Read original paper ↗ ·Improved material descriptors for bulk modulus in intermetallic compounds via machine learning
Rare Metals · 2023-05-12
Dexin Zhu, Kunming Pan, Yuan Wu, Xiaoye Zhou, Xiang‐Yue Li, Yongpeng Ren, Sairu Shi, Hua Yu, Shizhong Wei, Hong‐Hui Wu, Xusheng Yang
Read original paper ↗ ·Multi-objective optimization for high-performance Fe-based metallic glasses via machine learning approach
Journal of Alloys and Compounds · 2023-05-30
Yuxing Zhang, She-Juan Xie, Wei Peng Guo, Jun Cai Ding, Leong Hien Poh, Zhen-Dong Sha
Read original paper ↗ ·Interpretable glass forming ability prediction of amorphous alloys through tree based algorithms
Materials Letters · 2023-06-16
Ravindranadh Bobbili
Read original paper ↗ ·Materials genome strategy for metallic glasses
Journal of Material Science and Technology · 2023-06-20
Zhichao Lu, Yibo Zhang, Wenyue Li, Jinyue Wang, Xiongjun Liu, Yuan Wu, Hui Wang, Dong Ma, Zhaoping Lü
Read original paper ↗ ·Insights into metal glass forming ability based on data-driven analysis
Materials & Design · 2023-06-29
Tinghong Gao, Yong Ma, Yutao Liu, Qian Chen, Yongchao Liang, Quan Xie, Qingquan Xiao
Read original paper ↗ ·Does the GFA of Alloys Depend on the Atomic Size Ratio: A DT-Based ML Study
Crystal Growth & Design · 2023-07-10
Jiaming Pan, Yushuang Jia, Kenli Li, Lei Liao, Liping Qiu, Fan Wu, Zean Tian
Read original paper ↗ ·Concurrent prediction of metallic glasses’ global energy and internal structural heterogeneity by interpretable machine learning
Acta Materialia · 2023-08-25
Chaoyi Liu, Yuchu Wang, Yuchi Wang, Minhazul Islam, Jinwoo Hwang, Yunzhi Wang, Yue Fan
Read original paper ↗ ·Machine Learning Aided Prediction of Glass-Forming Ability of Metallic Glass
Processes · 2023-09-21
Chengcheng Liu, Xuandong Wang, Weidong Cai, Yazhou He, Hang Su
Read original paper ↗ ·A multi-strategy hybrid machine learning model for predicting glass-formation ability of metallic glasses based on imbalanced datasets
Journal of Non-Crystalline Solids · 2023-09-22
Xiaowei Liu, Zhilin Long, Wei Zhang, Lingming Yang, Zhuang Li
Read original paper ↗ ·Tribological Properties Assessment of Metallic Glasses Through a Genetic Algorithm-Optimized Machine Learning Model
Metals and Materials International · 2023-10-14
Untung Rahardja, Arif Sarı, Ali H. Alsalamy, Shavan K. Askar, Ahmed Hussien Alawadi, Barno Sayfutdinovna Abdullaeva
Read original paper ↗ ·A micromechanical nested machine learning model for characterizing materials behaviors of bulk metallic glasses
Journal of Non-Crystalline Solids · 2023-11-21
Moustafa Sahnoune Chaouche, Hani K. Al-Mohair, Shavan K. Askar, Barno Sayfutdinovna Abdullaeva, Naseer Ali Hussien, Ahmed Hussien Alawadi
Read original paper ↗ ·Customized design of amorphous solids by generative deep learning
The Innovation Materials · 2024-01-01
Yinghui Shang, Ziqing Zhou, Rong Han, Hang Wang, Xiaodi Liu, Yong Gang Yang
Read original paper ↗ ·Data-driven machine learning prediction of flow curve and Newtonian to non-Newtonian flow regime transition in bulk metallic glasses within supercooled liquid region
Journal of Non-Crystalline Solids · 2024-01-20
Pouria Mohammad, Mehdi Malekan, Alireza Jalali
Read original paper ↗ ·Alloys innovation through machine learning: a statistical literature review
Science and Technology of Advanced Materials Methods · 2024-03-04
Alireza Valizadeh, Ryoji Sahara, Maaouia Souissi
Read original paper ↗ ·Study of the novel boron nitride polymorphs: First- principles calculations and machine learning
Chinese Journal of Physics · 2024-04-24
Qingyang Fan, Wenjiao Li, Nan Chun Wu, Yingbo Zhao, Yanxing Song, Xinhai Yu, Sining Yun
Read original paper ↗ ·Identifying key features for predicting glass-forming ability of bulk metallic glasses via interpretable machine learning
Journal of Materials Science · 2024-05-01
Yangchuan Zeng, Zean Tian, Quan Zheng, Anguo Bu, Quan Xie
Read original paper ↗ ·Prediction of High-Entropy Alloy Phases Using Soft Computing Techniques
Advances in science and technology · 2024-07-23
Akeem Damilola Akinwekomi
Read original paper ↗ ·Artificial Intelligence in Biomaterials: A Comprehensive Review
Applied Sciences · 2024-07-28
Yasemin Gokcekuyu, Fatih Ekinci, Mehmet Serdar Güzel, Koray Açıcı, Sahin Aydin, Tunç Aşuroğlu
Read original paper ↗ ·Thermal effects on the mechanical behavior of CuZr metallic glasses
Applied Physics A · 2024-08-07
Nicolás Amigó, Claudio A. Careglio, Franco Ardiani, Andrés A. Manelli, Diego R. Tramontina, Eduardo M. Bringa
Read original paper ↗ ·Crystal structure graph neural networks for high-performance superconducting critical temperature prediction
Science China Materials · 2024-08-23
Jingzi Zhang, Chengquan Zhong, Xiaoting Lu, Jiakai Liu, Kailong Hu, Xi Lin
Read original paper ↗ ·Predictor−corrector inverse design scheme for property−composition prediction of amorphous alloys
Transactions of Nonferrous Metals Society of China · 2025-01-01
Tao LONG, Zhilin Long, Bo Pang
Read original paper ↗ ·Support Vector Machine
Springer eBooks · 2025-01-01
Tong‐Yi Zhang
Read original paper ↗ ·Machine learning-driven design of rare metal doped niobium alloys with enhanced strength and ductility
Journal of Materials Research and Technology · 2025-04-06
Zhenqiang Xiong, ZhaoKun Song, Jianwei Li, Heran Wang, Xiaoxin Zhang, Bin Liang, Dong Wang
Read original paper ↗ ·Application of Machine Learning in Amorphous Alloys
Materials · 2025-04-13
Like Zhang, Huangyou Zhang, Boyan Ji, Leqing Liu, Xianlan Liu, Ding Chen
Read original paper ↗ ·Machine learning prediction of metallic glass forming ability: The pivotal role of relative energy
Journal of Non-Crystalline Solids · 2025-04-21
Xiaohan Cheng, Ping Huang, Wang Fei
Read original paper ↗ ·Metallic glass roadmap
Materials Futures · 2025-04-23
Weihua Wang, Rui Zhao, Rong Han, Ying-Hui Shang, Yong Gang Yang, Songling Liu, Shiyun Zhang, Yuan‐Chao Hu, Yitao Sun, Ming-Xing Li, Lingxiang Shi, Ke-Fu Yao, Jiang Ma, Haibo Ke, Yong Zhao, Bo Zhang, Xin Tong, H. Y. Bai, Sinan Liu, Hai‐Bin Yu, Si Lan, Qi Cheng, Jihan Zhou, Heng Kang, Pengfei Guan, Zhenwei Wu, Huaping Zhang, Peng Luo, Laiquan Shen, Hai‐Bin Yu, Si Meng, Zheng Wang, Hailong Peng, Shuai Ren, Yu Tong, Lijian Song, Juntao Huo, Junqiang Wang, Jing-Li Ren, Peng Wang, Maozhi Li, Bei-Bei Fan, Bo Huang, Jun Yi, Xilei Bian, Qing Wang, Gang Wang, Minqiang Jiang, Yin-Xiao Wang, Zhengwang Zhu, Haifeng Zhang, Chuan-Kun Zhou, Ming Liu, Shaofan Zhao, Jing Zhou, Xuesong Li, Baoan Sun, Zhen Lu, Shu-Jie Pang, Hu-Yang Li, Huaijun Lin, Ji Wang, X S Wang, Yuhan Shen, Cheng-Rong Cao, B.Z. Tang, Peng Yu
Read original paper ↗ ·A computer vision-based model for predicting the glass-forming ability of alloys without feature engineering
Physica B Condensed Matter · 2025-05-08
Gongmin Wei, Yongchao Liang, Yuancheng Lin, Xin Fang, Jie Li, Qian Chen, Yunjun Ruan, Jia-mu Zhao, Shengli He, Junjie Zeng
Read original paper ↗ ·Multi-objective optimization of Fe-based metallic glasses on magnetic property, thermal stability and glass forming ability
Journal of Non-Crystalline Solids · 2025-06-19
Runyu Mo, Peng Wang, Jingli Ren
Read original paper ↗ ·Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review
Computational Condensed Matter · 2025-09-17
Dilshod Nematov, Mirabbos Hojamberdiev
Read original paper ↗ ·Enhanced permittivity prediction in microwave dielectric ceramics and structure-property insights via CGCNN combined with GNNExplainer
Materials Today Chemistry · 2025-10-01
Xiao-Bin Liu, Ning Shao, Qiuxia Huang, Lei Zhang, Ziheng Gao, Yapeng Tian, Haiquan Wang, Huanfu Zhou
Read original paper ↗ ·Assessing the potential of machine learning for predicting the nonlinear refractive index of glasses
Journal of Applied Physics · 2025-10-08
Murilo Neco Saraiva, Cléber Renato Mendonça
Read original paper ↗ ·Soft computing-based modelling of mechanical properties of titanium alloys
Canadian Metallurgical Quarterly · 2025-10-10
Akeem Damilola Akinwekomi
Read original paper ↗ ·Graph-Based Learning for Disordered Systems: A Review of Amorphous, Liquid, and Glassy Materials
Journal of Computational and Data-Driven Materials Engineering · 2026-01-01
Rashid Al-Mahdi, Khalifa Al-Suwaidi, Mariam Al-Kuwari
Read original paper ↗ ·Interpretable Machine-Learning-Guided Composition Design and Experimental Validation of Atomized Fe–Si–B–C Amorphous Soft-Magnetic Powders
SSRN Electronic Journal · 2026-01-01
Shiya Lv, Chunjiang Kuang, Lei Wang, Hongqiu Ma, Lingbing Meng
Read original paper ↗ ·Towards intelligent design of metallic glasses: a data-driven pathway for closing the theory-experiment loop
AI for Science · 2026-02-05
Huanrong Liu, Shan Zhang, Qingan Li, Bin Xu, Jian Li, Pengfei Guan
Read original paper ↗ ·New Opportunities For the Integration of Artificial Intelligence With Materials Science: From Large Language Models to Embodied Large Models
Materials Genome Engineering Advances · 2026-03-01
Zhen Song, Yishuai Cai, Yadong Yu, Pan Xiang, Minglong Li, Zhe Liu, Mengyan Dai
Read original paper ↗ ·From empirical rules to data-driven design of Al-based amorphous alloys
Journal of Alloys and Compounds · 2026-03-22
Xili liu, Jiahao Luo, Enhao Zhang, Enhao Zhang, Zhiyuan Jing, Wengang Bu, Jiamao Hao, Zhibin Zhang, Qiuzhi Gao, Xiubing Liang
Read original paper ↗ ·Small-data machine learning-assisted design of synergistic phase change composites for advanced thermal management
Composites Science and Technology · 2026-06-03
Bohao Xu, Weihe Chen, Siyang Wang, Yonglin Chen, Weidong Yang, Yan Li
Read original paper ↗ ·Machine learning applications to bioactive glasses: From traditional statistical models to cutting-edge deep learning paradigms
Journal of Non-Crystalline Solids · 2026-06-22
Amirhossein Moghanian, Ahmet Akif Kızılkurtlu, Mohammad Nasr Esfahani, Arang Pazhouheshgar, Francesco Baino, Ali Akpek, Sirus Safaee
Read original paper ↗ ·