BetaCitation Atlas

RESEARCH PAPER · MRS Communications · 2019

Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses

Jie Xiong, Tong‐Yi Zhang, San‐Qiang Shi

Read original paper ↗

See where this research leads.

96 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glassesOriginalMachine learning prediction of the hardness of tool and mold steels10A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys285Critical feature space for predicting the glass forming ability of metallic alloys revealed by machine learning60Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach52Machine learning-based glass formation prediction in multicomponent alloys133Design of Fe-based bulk metallic glasses for maximum amorphous diameter (Dmax) using machine learning models50Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques32Exploration of Characteristic Temperature Contributions to Metallic Glass Forming Ability0Modelling of phase stability: integrating computational materials science and materials informatics3Deep learning-assisted elastic isotropy identification for architected materials29A general and transferable deep learning framework for predicting phase formation in materials93Machine learning prediction of glass-forming ability in bulk metallic glasses58Multi-objective machine learning of four mechanical properties of steels14Machine learning prediction of magnetic properties of Fe-based metallic glasses considering local structures1Machine learning of phases and mechanical properties in complex concentrated alloys162Determination of glass forming ability of bulk metallic glasses based on machine learning58A two-step fused machine learning approach for the prediction of glass-forming ability of metallic glasses34Exploration of characteristic temperature contributions to metallic glass forming ability13A focused review on machine learning aided high-throughput methods in high entropy alloy134Predicting the glass formation of metallic glasses using machine learning approaches25Engineering of Novel Fe-Based Bulk Metallic Glasses Using a Machine Learning-Based Approach28Machine learning for alloys698Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning50Machine learning prediction of magnetic properties of Fe-based metallic glasses considering glass forming ability75Correlative study between elastic modulus and glass formation in ZrCuAl(X) amorphous system using a machine learning approach4Searching for Mechanically Superior Solid-State Electrolytes in Li-Ion BatteriesviaData-Driven Approaches60Explicit expressions of the saturation flux density and thermal stability in Fe-based metallic glasses based on Lasso regression17Molecular dynamic characteristic temperatures for predicting metallic glass forming ability8Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges67Machine learning prediction of thermodynamic and mechanical properties of multicomponent Fe-Cr-based alloys24Balancing data for generalizable machine learning to predict glass-forming ability of ternary alloys34Recent development of chemically complex metallic glasses: from accelerated compositional design, additive manufacturing to novel applications44A critical review of the machine learning guided design of metallic glasses for superior glass-forming ability13Machine-learning improves understanding of glass formation in metallic systems10SISSO-assisted prediction and design of mechanical properties of porous graphene with a uniform nanopore array25Key feature space for predicting the glass-forming ability of amorphous alloys revealed by gradient boosted decision trees model51Progress and Perspective of Metallic Glasses for Energy Conversion and Storage71Unification of characteristic temperature-based glass-forming ability and stability criteria3Application of machine learning for advanced material prediction and design123ML Model Optimization–Selection and GFA Prediction for Binary Alloys5Data-driven glass-forming ability criterion for bulk amorphous metals with data augmentation54Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys36Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features24Novel Alloy Design Concepts Enabling Enhanced Mechanical Properties of High Entropy Alloys40Machine learning driven rationally design of amorphous alloy with improved elastic models34Prediction of glass forming ability of bulk metallic glasses based on convolutional neural network31Machine Learning-Guided Exploration of Glass-Forming Ability in Multicomponent Alloys2Machine Learning Based Sintered Density Prediction of Bronze Processed by Powder Metallurgy Route22Machine learning assisted design of aluminum-lithium alloy with high specific modulus and specific strength56Understanding the metallic glasses formation by applying a centrality measure based on betweenness1Data-driven machine learning prediction of glass transition temperature and the glass-forming ability of metallic glasses15An ensemble learning based amorphous state predictor for multicomponent alloys7A generative deep learning framework for inverse design of compositionally complex bulk metallic glasses66Latest Advances in Manufacturing and Machine Learning of Bulk Metallic Glasses14Overcoming the challenge of the data imbalance for prediction of the glass forming ability in bulk metallic glasses16Chemical composition analysis of ancient glass products based on decision tree2Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties7Improved material descriptors for bulk modulus in intermetallic compounds via machine learning27Multi-objective optimization for high-performance Fe-based metallic glasses via machine learning approach15Interpretable glass forming ability prediction of amorphous alloys through tree based algorithms7Materials genome strategy for metallic glasses19Insights into metal glass forming ability based on data-driven analysis15Does the GFA of Alloys Depend on the Atomic Size Ratio: A DT-Based ML Study3Concurrent prediction of metallic glasses’ global energy and internal structural heterogeneity by interpretable machine learning39Machine Learning Aided Prediction of Glass-Forming Ability of Metallic Glass18A multi-strategy hybrid machine learning model for predicting glass-formation ability of metallic glasses based on imbalanced datasets11Tribological Properties Assessment of Metallic Glasses Through a Genetic Algorithm-Optimized Machine Learning Model52A micromechanical nested machine learning model for characterizing materials behaviors of bulk metallic glasses10Customized design of amorphous solids by generative deep learning6Data-driven machine learning prediction of flow curve and Newtonian to non-Newtonian flow regime transition in bulk metallic glasses within supercooled liquid region7Alloys innovation through machine learning: a statistical literature review14Study of the novel boron nitride polymorphs: First- principles calculations and machine learning6Identifying key features for predicting glass-forming ability of bulk metallic glasses via interpretable machine learning6Prediction of High-Entropy Alloy Phases Using Soft Computing Techniques2Artificial Intelligence in Biomaterials: A Comprehensive Review120Thermal effects on the mechanical behavior of CuZr metallic glasses8Crystal structure graph neural networks for high-performance superconducting critical temperature prediction5Predictor−corrector inverse design scheme for property−composition prediction of amorphous alloys2Support Vector Machine4Machine learning-driven design of rare metal doped niobium alloys with enhanced strength and ductility6Application of Machine Learning in Amorphous Alloys4Machine learning prediction of metallic glass forming ability: The pivotal role of relative energy1Metallic glass roadmap26A computer vision-based model for predicting the glass-forming ability of alloys without feature engineering2Multi-objective optimization of Fe-based metallic glasses on magnetic property, thermal stability and glass forming ability3Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review50Enhanced permittivity prediction in microwave dielectric ceramics and structure-property insights via CGCNN combined with GNNExplainer2Assessing the potential of machine learning for predicting the nonlinear refractive index of glasses1Soft computing-based modelling of mechanical properties of titanium alloys1Graph-Based Learning for Disordered Systems: A Review of Amorphous, Liquid, and Glassy Materials0Interpretable Machine-Learning-Guided Composition Design and Experimental Validation of Atomized Fe–Si–B–C Amorphous Soft-Magnetic Powders0Towards intelligent design of metallic glasses: a data-driven pathway for closing the theory-experiment loop1New Opportunities For the Integration of Artificial Intelligence With Materials Science: From Large Language Models to Embodied Large Models2From empirical rules to data-driven design of Al-based amorphous alloys0Small-data machine learning-assisted design of synergistic phase change composites for advanced thermal management1Machine learning applications to bioactive glasses: From traditional statistical models to cutting-edge deep learning paradigms0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

96 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Machine learning prediction of the hardness of tool and mold steels

    Scientia Sinica Technologica · 2019-10-01

    Tong‐Yi Zhang, YanLin HE, JiaHao WANG, Sheng Sun

    Read original paper ↗ ·

  2. A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys

    Materials & Design · 2019-11-23

    Jie Xiong, San‐Qiang Shi, Tong‐Yi Zhang

    Read original paper ↗ ·

  3. Critical feature space for predicting the glass forming ability of metallic alloys revealed by machine learning

    Chemical Physics · 2020-06-12

    Binghui Deng, Yali Zhang

    Read original paper ↗ ·

  4. Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach

    Computational Materials Science · 2020-09-04

    Majid Samavatian, Reza Gholamipour, Vahid Samavatian

    Read original paper ↗ ·

  5. Machine learning-based glass formation prediction in multicomponent alloys

    Acta Materialia · 2020-10-04

    Xiaodi Liu, Xin Li, Quanfeng He, Dandan Liang, Ziqing Zhou, Jiang Ma, Yong Gang Yang, Jun Shen

    Read original paper ↗ ·

  6. Design of Fe-based bulk metallic glasses for maximum amorphous diameter (Dmax) using machine learning models

    Computational Materials Science · 2020-12-25

    Daniel Mastropietro, Javier Alberto Moya

    Read original paper ↗ ·

  7. Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

    Journal of Materials Informatics · 2021-01-01

    Yi Wang, Guangchen Liu, Jianbao Gao, Lijun Zhang

    Read original paper ↗ ·

  8. Exploration of Characteristic Temperature Contributions to Metallic Glass Forming Ability

    Figshare · 2021-01-01

    Dane D. Morgan, Lane E. Schultz, Paul M. Voyles, Izabela Szlufarska, Carter Francis, Ben2 Afflerbach

    Read original paper ↗ ·

  9. Modelling of phase stability: integrating computational materials science and materials informatics

    Journal of Materials Informatics · 2021-01-01

    Xiaoyan Song, Kai Guo, Hao Lu, Dong Liu, Fawei Tang

    Read original paper ↗ ·

  10. Deep learning-assisted elastic isotropy identification for architected materials

    Extreme Mechanics Letters · 2021-01-12

    Anran Wei, Jie Xiong, Weidong Yang, Fenglin Guo

    Read original paper ↗ ·

  11. A general and transferable deep learning framework for predicting phase formation in materials

    npj Computational Materials · 2021-01-25

    Shuo Feng, Huadong Fu, Huiyu Zhou, Yuan Wu, Zhaoping Lü, Hongbiao Dong

    Read original paper ↗ ·

  12. Machine learning prediction of glass-forming ability in bulk metallic glasses

    Computational Materials Science · 2021-02-19

    Jie Xiong, San‐Qiang Shi, Tong‐Yi Zhang

    Read original paper ↗ ·

  13. Multi-objective machine learning of four mechanical properties of steels

    Scientia Sinica Technologica · 2021-03-05

    Qinghua Wei, Jie Xiong, Sheng Sun, Tong‐Yi Zhang

    Read original paper ↗ ·

  14. Machine learning prediction of magnetic properties of Fe-based metallic glasses considering local structures

    arXiv (Cornell University) · 2021-03-16

    Xin Li, Guangcun Shan, C.H. Shek

    Read original paper ↗ ·

  15. Machine learning of phases and mechanical properties in complex concentrated alloys

    Journal of Material Science and Technology · 2021-03-18

    Jie Xiong, San‐Qiang Shi, Tong‐Yi Zhang

    Read original paper ↗ ·

  16. Determination of glass forming ability of bulk metallic glasses based on machine learning

    Computational Materials Science · 2021-04-15

    Peng Li, Zhilin Long, Mingshengzi Zhao

    Read original paper ↗ ·

  17. A two-step fused machine learning approach for the prediction of glass-forming ability of metallic glasses

    Journal of Alloys and Compounds · 2021-04-22

    Y.X. Zhang, Guichuan Xing, Zhen-Dong Sha, Leong Hien Poh

    Read original paper ↗ ·

  18. Exploration of characteristic temperature contributions to metallic glass forming ability

    Computational Materials Science · 2021-05-06

    Lane E. Schultz, Benjamin T. Afflerbach, Carter Francis, Paul M. Voyles, Izabela Szlufarska, Dane D. Morgan

    Read original paper ↗ ·

  19. A focused review on machine learning aided high-throughput methods in high entropy alloy

    Journal of Alloys and Compounds · 2021-05-11

    Ling Qiao, Yong Liu, Jingchuan Zhu

    Read original paper ↗ ·

  20. Predicting the glass formation of metallic glasses using machine learning approaches

    Computational Materials Science · 2021-06-22

    Zhuang Li, Zhilin Long, Shan Lei, Ting Zhang, Xiaowei Liu, Du-min Kuang

    Read original paper ↗ ·

  21. Engineering of Novel Fe-Based Bulk Metallic Glasses Using a Machine Learning-Based Approach

    Arabian Journal for Science and Engineering · 2021-07-17

    Tzu-Chia Chen, Rajiman Rajiman, Marischa Elveny, John William Grimaldo Guerrero, Adedoyin Isola Lawal, Ngakan Ketut Acwin Dwijendra, Aravindhan Surendar, Svetlana Dmitrievna Danshina, Yu Zhu

    Read original paper ↗ ·

  22. Machine learning for alloys

    Nature Reviews Materials · 2021-07-20

    Gus L. W. Hart, Tim Mueller, Cormac Toher, Stefano Curtarolo A

    Read original paper ↗ ·

  23. Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning

    npj Computational Materials · 2021-08-23

    Ziqing Zhou, Quanfeng He, Xiaodi Liu, Qiang Wang, Junhua Luan, C.T. Liu, Yong Gang Yang

    Read original paper ↗ ·

  24. Machine learning prediction of magnetic properties of Fe-based metallic glasses considering glass forming ability

    Journal of Material Science and Technology · 2021-08-27

    Xin Li, Guangcun Shan, C.H. Shek

    Read original paper ↗ ·

  25. Correlative study between elastic modulus and glass formation in ZrCuAl(X) amorphous system using a machine learning approach

    Applied Physics A · 2021-08-28

    Jianqing Li, Tzu-Chia Chen, Angelina Olegovna Zekiy

    Read original paper ↗ ·

  26. Searching for Mechanically Superior Solid-State Electrolytes in Li-Ion BatteriesviaData-Driven Approaches

    ACS Applied Materials & Interfaces · 2021-09-02

    Eunseong Choi, Junho Jo, Won-Jin Kim, Kyoungmin Min

    Read original paper ↗ ·

  27. Explicit expressions of the saturation flux density and thermal stability in Fe-based metallic glasses based on Lasso regression

    Intermetallics · 2021-09-19

    Zhuang Li, Zhilin Long, Shan Lei, Lingming Yang, Wei Zhang, Ting Zhang

    Read original paper ↗ ·

  28. Molecular dynamic characteristic temperatures for predicting metallic glass forming ability

    Computational Materials Science · 2021-09-30

    Lane E. Schultz, Benjamin T. Afflerbach, Izabela Szlufarska, Dane D. Morgan

    Read original paper ↗ ·

  29. Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges

    Materials · 2021-10-02

    Karol Frydrych, Kamran Karimi, Michal Pecelerowicz, Rene Alvarez, Francesco Javier Dominguez-Gutiérrez, Fabrizio Rovaris, Stefanos Papanikolaou

    Read original paper ↗ ·

  30. Machine learning prediction of thermodynamic and mechanical properties of multicomponent Fe-Cr-based alloys

    Physical Review Materials · 2021-10-18

    B. O. Mukhamedov, K. V. Karavaev, Igor A. Abrikosov

    Read original paper ↗ ·

  31. Balancing data for generalizable machine learning to predict glass-forming ability of ternary alloys

    Scripta Materialia · 2021-10-30

    Yi Yao, Timothy Sullivan, Feng Yan, Jiaqi Gong, Lin Li

    Read original paper ↗ ·

  32. Recent development of chemically complex metallic glasses: from accelerated compositional design, additive manufacturing to novel applications

    Materials Futures · 2021-12-21

    Jingyang Zhang, Z Q Zhou, Zhibing Zhang, M H Park, Qingliang Yu, Zi‐Jian Li, Jiang Ma, Anding Wang, H G Huang, Min Jung Song, B S Guo, Qing Wang, Yong Gang Yang

    Read original paper ↗ ·

  33. A critical review of the machine learning guided design of metallic glasses for superior glass-forming ability

    Journal of Materials Informatics · 2022-01-01

    Ziqing Zhou, Yinghui Shang, Yong Yang

    Read original paper ↗ ·

  34. Machine-learning improves understanding of glass formation in metallic systems

    Digital Discovery · 2022-01-01

    Robert M. Forrest, Alan Lindsay Greer

    Read original paper ↗ ·

  35. SISSO-assisted prediction and design of mechanical properties of porous graphene with a uniform nanopore array

    Nanoscale Advances · 2022-01-01

    Anran Wei, Han Ye, Zhenlin Guo, Jie Xiong

    Read original paper ↗ ·

  36. Key feature space for predicting the glass-forming ability of amorphous alloys revealed by gradient boosted decision trees model

    Journal of Alloys and Compounds · 2022-01-06

    X.W. Liu, Zhilin Long, W. Zhang, L.M. Yang

    Read original paper ↗ ·

  37. Progress and Perspective of Metallic Glasses for Energy Conversion and Storage

    Advanced Energy Materials · 2022-01-18

    Rui Jiang, Yumin Da, Zelin Chen, Xiaoya Cui, Xiaopeng Han, Haibo Ke, Yanhui Liu, Yanan Chen, Yida Deng, Wenbin Hu

    Read original paper ↗ ·

  38. Unification of characteristic temperature-based glass-forming ability and stability criteria

    Journal of Non-Crystalline Solids · 2022-02-25

    Xiaocheng Li, Shengzhong Kou, Xiaoqiang Fu, Xi-Lai Jiang, Chunling Li, Chunyan Li, Yanchun Zhao, Yutian Ding

    Read original paper ↗ ·

  39. Application of machine learning for advanced material prediction and design

    EcoMat · 2022-03-07

    Cheuk Hei Chan, Mingzi Sun, Bolong Huang

    Read original paper ↗ ·

  40. ML Model Optimization–Selection and GFA Prediction for Binary Alloys

    Crystal Growth & Design · 2022-03-09

    Jiaming Pan, Jiang Xiao, Zean Tian, Yikun Hu, Kenli Li

    Read original paper ↗ ·

  41. Data-driven glass-forming ability criterion for bulk amorphous metals with data augmentation

    Journal of Material Science and Technology · 2022-03-15

    Jie Xiong, Tong‐Yi Zhang

    Read original paper ↗ ·

  42. Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys

    Computational Materials Science · 2022-03-17

    Cameron McElfresh, Collin Roberts, Sicong He, Sergey V. Prikhodko, Jaime Marian

    Read original paper ↗ ·

  43. Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features

    Chemistry of Materials · 2022-03-30

    Benjamin T. Afflerbach, Carter Francis, Lane E. Schultz, Janine T. Spethson, Vanessa Meschke, Elliot J. Strand, Logan T. Ward, John H. Perepezko, Dan J. Thoma, Paul M. Voyles, Izabela Szlufarska, Dane D. Morgan

    Read original paper ↗ ·

  44. Novel Alloy Design Concepts Enabling Enhanced Mechanical Properties of High Entropy Alloys

    Frontiers in Materials · 2022-06-06

    Jitesh Kumar, Saumya Ranjan Jha, Abheepsit Raturi, Anurag Bajpai, Reshma Sonkusare, Nilesh Prakash Gurao, Krishanu Biswas

    Read original paper ↗ ·

  45. Machine learning driven rationally design of amorphous alloy with improved elastic models

    Materials & Design · 2022-06-22

    Zhuang Li, Zhilin Long, Shan Lei, Yulin Tang

    Read original paper ↗ ·

  46. Prediction of glass forming ability of bulk metallic glasses based on convolutional neural network

    Journal of Non-Crystalline Solids · 2022-08-11

    Ting Zhang, Zhilin Long, Peng Li, Zhuang Li

    Read original paper ↗ ·

  47. Machine Learning-Guided Exploration of Glass-Forming Ability in Multicomponent Alloys

    JOM · 2022-10-14

    Yi Yao, Timothy Sullivan, Feng Yan, Jiaqi Gong, Lin Li

    Read original paper ↗ ·

  48. Machine Learning Based Sintered Density Prediction of Bronze Processed by Powder Metallurgy Route

    Metals and Materials International · 2022-11-26

    Tafzeelul Kamal, Gouthama, Anish Upadhyaya

    Read original paper ↗ ·

  49. Machine learning assisted design of aluminum-lithium alloy with high specific modulus and specific strength

    Materials & Design · 2022-12-09

    Huiyu Li, Xiwu Li, Yanan Li, Wei Xiao, Kai Wen, Zhihui Li, Yongan Zhang, Baiqing Xiong

    Read original paper ↗ ·

  50. Understanding the metallic glasses formation by applying a centrality measure based on betweenness

    Computational Materials Science · 2022-12-23

    Rocío Rodríguez, Manuel Curado, Leandro Tortosa, José F. Vicent

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.