BetaCitation Atlas

RESEARCH PAPER · 中国科学通报：英文版 · 2019

Predicting the onset temperature (Tg) of Ge Se1− glass transition: a feature selection based two-stage support vector regression method

Yue Liu, Junming Wu, Guang Yang, Tianlu Zhao, Siqi Shi

Read original paper ↗

See where this research leads.

71 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

Predicting the onset temperature (Tg) of Ge Se1− glass transition: a feature selection based two-stage support vector regression methodOriginalMachine Learning Stability and Bandgaps of Lead‐Free Perovskites for Photovoltaics64Ultralow voltage imprinting in GeS2–Ga2S3–AgI glasses for visible to middle-infrared diffraction gratings11Formulation of mix design for 3D printing of geopolymers: a machine learning approach26Multi‐Layer Feature Selection Incorporating Weighted Score‐Based Expert Knowledge toward Modeling Materials with Targeted Properties137Machine‐Learning‐Based Image Similarity Analysis for Use in Materials Characterization5Metaheuristic-based inverse design of materials – A survey46Imprinting gradient refractive index micro-structure in GeS2–Ga2S3–KCl glass for broadband diffraction grating10Bayesian optimization based on a unified figure of merit for accelerated materials screening: A case study of halide perovskites45Glass genetic engineering1Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning271A novel type of neural networks for feature engineering of geological data: Case studies of coal and gas hydrate-bearing sediments48Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry62Extreme learning machine and swarm-based support vector regression methods for predicting crystal lattice parameters of pseudo-cubic/cubic perovskites28Predicting As$_{x}$Se$_{1-x}$ Glass Transition Onset Temperature49High-throughput computational screening of oxide double perovskites for optoelectronic and photocatalysis applications49Machine learning in materials design: Algorithm and application*62Deposition and characterization of solution processed Se-rich Ge-Se thin films with specular optical quality using multi-component solvent approach14Machine learning glass transition temperature of styrenic random copolymers94Accelerating materials discovery using machine learning238Application of deep learning to inverse design of phase separation structure in polymer alloy52Density functional theory-free descriptor for the practical discovery of perovskite catalysts10Machine learning aided design of perovskite oxide materials for photocatalytic water splitting156Modeling superconducting transition temperature of doped MgB2 superconductor from structural distortion and ambient temperature resistivity measurement using hybrid intelligent approach16Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors118Machine Learning Boosting the Development of Advanced Lithium Batteries68Predictions of glass transition onset temperature of chalcogenide glass Ge Se1−21Machine Learning‐Aided Process Design: Modeling and Prediction of Transformation Temperature for Pearlitic Steel26Effect of composition and phase diagram features on printability and microstructure in laser powder bed fusion: Development and comparison of processing maps across alloy systems27A Property‐Driven Stepwise Design Strategy for Multiple Low‐Melting Alloys via Machine Learning26Data-driven shape memory alloy discovery using Artificial Intelligence Materials Selection (AIMS) framework76Data‐Driven Materials Innovation and Applications187Structural and dynamic properties of MgO–Al2O3–SiO2 glasses from molecular dynamics simulations and NMR34A review on Machine learning aspect in physics and mechanics of glasses44Properties and mechanism of amorphous lead aluminosilicate passivation layers used in semiconductor devices through molecular dynamic simulation5Effects of alkali metal ion on imprinting GRIN microstructure in GeS2-Ga2S3-MCl (M=Na, K, Cs) glasses for visible to mid-infrared microgratings9Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy7Identification and ranking biomaterials for bone scaffolds using machine learning and PROMETHEE32Classification and prediction model of ancient glass based on AP-CART0Inverse design of polymer alloys using deep learning based on self-consistent field analysis and finite element analysis12Scope of machine learning in materials research—A review230Prediction of Phase Composition and Process Resilience in Plasma‐Assisted Hetero‐Aggregate Synthesis using a Machine‐Learning Model with Multivariate Output7Development of a deep-learning model for phase-separation structure of diblock copolymer based on self-consistent field analysis3Highly effective design of high GFA alloys with different metal-based and various components by machine learning14Machine learning-accelerated discovery and design of electrode materials and electrolytes for lithium ion batteries93Deep learning-aided inverse analysis framework to accelerate the exploration of DP steel microstructures7Beyond the Surface: Interconnection of Viscosity, Crystal Growth, and Diffusion in Ge25Se75 Glass-Former4State-of-the-art review on various applications of machine learning techniques in materials science and engineering26Enhancing Copper Interconnect Reliability Based on Machine Learning: Electromigration Lifetime Prediction Via an -Interpretable Qpso-Xgboost Hybrid Model0Machine Learning Based Prediction of Creep Life for Nickel-Based Single Crystal Superalloys1Optimized batch identification of metallic alloy materials for precision machining operations using machine learning and image processing techniques0Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects51Interpretable machine learning for predicting properties of multi-component oxide glasses and experimental validation8Effects of alkali ion transverse migration on imprinting GRIN microstructure in GeS2-Ga2S3–NaCl glasses for visible to mid-infrared microlens arrays0Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review50A review on flow interactions analysis using machine learning-driven CFD modelling for offshore structures and marine applications1Advanced Machine Learning Interatomic Potential for Accelerated Atomistic Simulations of Lithiation Dynamics in Large-Scale Si@C Core–Shell Anodes1Coupling Data‐Driven and Reinforcement Learning for Material Development and Device Management in Batteries4A review of topological descriptors for amorphous materials complementing graph neural networks2Data driven intelligent research and development of gel electrolyte: Methods, challenges and cutting-edge trends1Graph representation learning for predicting creep rupture life in Nickel-Based single crystal superalloys0A closed-loop machine-learning framework for rational design of manganese-based nano-adsorbents3The role and strategies of data-driven machine learning in design optimization and performance evaluation of electrochemical energy storage materials0Data-driven chemical feature extraction and material design for halide solid-state electrolytes2Application of Machine Learning in the Study of Creep Properties of Alloys0Machine learning-driven prediction and validation for ionic conductivity of hydrogel electrolytes1Intelligent Computing for Future Layered Cathodes in Sodium‐Ion Batteries0Machine learning for cracking the molecular code of room-temperature organic phosphorescent materials: From structure-property relationship to performance prediction0ECNet: Spin-aware electronic-configuration encoding for composition-based prediction of the thermodynamic stability of inorganic compounds0Fracture prediction of 5052 aluminum alloy sheet forming using machine learning-calibrated DF-2013 uncoupled ductile fracture criterion0Artificial intelligence-assisted investigation of ion transport mechanisms and material screening for solid-state electrolytes0A document-level information extraction pipeline for high-entropy layered cathode materials in sodium-ion batteries0

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

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

  1. Machine Learning Stability and Bandgaps of Lead‐Free Perovskites for Photovoltaics

    Advanced Theory and Simulations · 2019-11-18

    Jared C. Stanley, Felix Mayr, Alessio Gagliardi

    Read original paper ↗ ·

  2. Ultralow voltage imprinting in GeS2–Ga2S3–AgI glasses for visible to middle-infrared diffraction gratings

    Ceramics International · 2019-12-17

    Guang Yang, Yunhang Qi, Jin Cao, Yinsheng Xu, Bin Liu, Hongfei Chen, Shixun Dai, Dongfeng Qi, Viktor Takáts, Yanfeng Gao

    Read original paper ↗ ·

  3. Formulation of mix design for 3D printing of geopolymers: a machine learning approach

    Materials Advances · 2020-01-01

    Ali Bagheri, Christian Crémona

    Read original paper ↗ ·

  4. Multi‐Layer Feature Selection Incorporating Weighted Score‐Based Expert Knowledge toward Modeling Materials with Targeted Properties

    Advanced Theory and Simulations · 2020-01-15

    Yue Liu, Junming Wu, Maxim Avdeev, Siqi Shi

    Read original paper ↗ ·

  5. Machine‐Learning‐Based Image Similarity Analysis for Use in Materials Characterization

    Advanced Theory and Simulations · 2020-01-21

    Zhilei Wang, Toshio Ogawa, Yoshitaka Adachi

    Read original paper ↗ ·

  6. Metaheuristic-based inverse design of materials – A survey

    Journal of Materiomics · 2020-02-22

    Thunshun Warren Liao, Guoqiang Li

    Read original paper ↗ ·

  7. Imprinting gradient refractive index micro-structure in GeS2–Ga2S3–KCl glass for broadband diffraction grating

    Optical Materials · 2020-03-01

    Guang Yang, Jin Cao, Yunhang Qi, Xiaoyan He, Changzhe Peng, Yunjun Lu, Feng Tang, Ke Tang, Bin Liu, Hongfei Chen, Guorong Chen, Yanfeng Gao, Dongchu Chen

    Read original paper ↗ ·

  8. Bayesian optimization based on a unified figure of merit for accelerated materials screening: A case study of halide perovskites

    Science China Materials · 2020-03-16

    Xiwen Chen, Chen Wang, Zhenzhu Li, Zhufeng Hou, Wan‐Jian Yin

    Read original paper ↗ ·

  9. Glass genetic engineering

    Scientia Sinica Technologica · 2020-03-23

    Zhongmin Yang, Guowu Tang, Zhonghong Jiang, Guoquan Qian, Qi Qian

    Read original paper ↗ ·

  10. Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning

    Acta Materialia · 2020-05-17

    Yue Liu, Junming Wu, Zhichao Wang, Xiao‐Gang Lu, Maxim Avdeev, Siqi Shi, Chong‐Yu Wang, Tao Yu

    Read original paper ↗ ·

  11. A novel type of neural networks for feature engineering of geological data: Case studies of coal and gas hydrate-bearing sediments

    Geoscience Frontiers · 2020-05-22

    Lishuai Jiang, Yang Zhao, Naser Golsanami, Lianjun Chen, Weichao Yan

    Read original paper ↗ ·

  12. Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry

    Journal of Energy Chemistry · 2020-05-27

    Yongqiang Kang, Lejing Li, Baohua Li

    Read original paper ↗ ·

  13. Extreme learning machine and swarm-based support vector regression methods for predicting crystal lattice parameters of pseudo-cubic/cubic perovskites

    Journal of Applied Physics · 2020-06-23

    Taoreed Olakunle Owolabi

    Read original paper ↗ ·

  14. Predicting As$$_{x}$$Se$$_{1-x}$$ Glass Transition Onset Temperature

    International Journal of Thermophysics · 2020-09-02

    Yun Zhang, Xiaojie Xu

    Read original paper ↗ ·

  15. High-throughput computational screening of oxide double perovskites for optoelectronic and photocatalysis applications

    Journal of Energy Chemistry · 2020-09-04

    Xiaowei Jiang, Wan‐Jian Yin

    Read original paper ↗ ·

  16. Machine learning in materials design: Algorithm and application*

    Chinese Physics B · 2020-10-14

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

    Read original paper ↗ ·

  17. Deposition and characterization of solution processed Se-rich Ge-Se thin films with specular optical quality using multi-component solvent approach

    Optical Materials Express · 2020-10-22

    Stanislav Šlang, Karel Pálka, Jiří Jančálek, Michal Kurka, Miroslav Vlček

    Read original paper ↗ ·

  18. Machine learning glass transition temperature of styrenic random copolymers

    Journal of Molecular Graphics and Modelling · 2020-11-09

    Yun Zhang, Xiaojie Xu

    Read original paper ↗ ·

  19. Accelerating materials discovery using machine learning

    Journal of Material Science and Technology · 2020-12-24

    Yongfei Juan, Yongbing Dai, Yang Yang, Jiao Zhang

    Read original paper ↗ ·

  20. Application of deep learning to inverse design of phase separation structure in polymer alloy

    Computational Materials Science · 2021-01-18

    Kazuya Hiraide, Kenta Hirayama, Katsuhiro Endo, Mayu Muramatsu

    Read original paper ↗ ·

  21. Density functional theory-free descriptor for the practical discovery of perovskite catalysts

    Computational Materials Science · 2021-02-08

    Wan‐Jian Yin

    Read original paper ↗ ·

  22. Machine learning aided design of perovskite oxide materials for photocatalytic water splitting

    Journal of Energy Chemistry · 2021-02-10

    Qiuling Tao, Tian Lu, Sheng Ye, Long Li, Wencong Lu, Minjie Li

    Read original paper ↗ ·

  23. Modeling superconducting transition temperature of doped MgB2 superconductor from structural distortion and ambient temperature resistivity measurement using hybrid intelligent approach

    Computational Materials Science · 2021-03-02

    Sunday Olusanya Olatunji, Taoreed Olakunle Owolabi

    Read original paper ↗ ·

  24. Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors

    中国科学通报：英文版 · 2021-04-23

    Qian Zhao, Maxim Avdeev, Liquan Chen, Siqi Shi

    Read original paper ↗ ·

  25. Machine Learning Boosting the Development of Advanced Lithium Batteries

    Small Methods · 2021-07-10

    Yangting Liu, Qian Zhou, Guanglei Cui

    Read original paper ↗ ·

  26. Predictions of glass transition onset temperature of chalcogenide glass Ge Se1−

    Journal of Physics and Chemistry of Solids · 2021-07-14

    Yun Zhang, Xiaojie Xu

    Read original paper ↗ ·

  27. Machine Learning‐Aided Process Design: Modeling and Prediction of Transformation Temperature for Pearlitic Steel

    steel research international · 2021-08-09

    Ling Qiao, Jingchuan Zhu, Yuan Wang

    Read original paper ↗ ·

  28. Effect of composition and phase diagram features on printability and microstructure in laser powder bed fusion: Development and comparison of processing maps across alloy systems

    Additive manufacturing · 2021-08-18

    Raiyan Seede, Jiahui Ye, Austin Whitt, William Trehern, Alaa H. Elwany, Raymundo Arróyave, İbrahim Karaman

    Read original paper ↗ ·

  29. A Property‐Driven Stepwise Design Strategy for Multiple Low‐Melting Alloys via Machine Learning

    Advanced Engineering Materials · 2021-08-24

    Huimin Chen, Zhongwen Shang, Wencong Lu, Minjie Li, Fuping Tan

    Read original paper ↗ ·

  30. Data-driven shape memory alloy discovery using Artificial Intelligence Materials Selection (AIMS) framework

    Acta Materialia · 2022-02-12

    William Trehern, R. Ortiz-Ayala, Kadri Can Atli, Raymundo Arróyave, İbrahim Karaman

    Read original paper ↗ ·

  31. Data‐Driven Materials Innovation and Applications

    Advanced Materials · 2022-04-22

    Zhuo Wang, Zhehao Sun, Hang Yin, Xinghui Liu, Jinlan Wang, Haitao Zhao, Cheng Heng Pang, Tao Wu, Shuzhou Li, Zongyou Yin, Xue‐Feng Yu

    Read original paper ↗ ·

  32. Structural and dynamic properties of MgO–Al2O3–SiO2 glasses from molecular dynamics simulations and NMR

    Ceramics International · 2022-04-26

    Chun-Yu Chen, Cong Zhong, Yan Zhang, Ao Li, Sanxi Huang, Huidan Zeng, Qun Zu

    Read original paper ↗ ·

  33. A review on Machine learning aspect in physics and mechanics of glasses

    Materials Science and Engineering B · 2022-07-13

    Jashanpreet Singh, Simranjit Singh

    Read original paper ↗ ·

  34. Properties and mechanism of amorphous lead aluminosilicate passivation layers used in semiconductor devices through molecular dynamic simulation

    Ceramics International · 2022-07-30

    Cong Zhong, Ao Li, Jingtao Yan, Qi Jiang, Rui Yang, Kejun Lu, Huidan Zeng

    Read original paper ↗ ·

  35. Effects of alkali metal ion on imprinting GRIN microstructure in GeS2-Ga2S3-MCl (M=Na, K, Cs) glasses for visible to mid-infrared microgratings

    Ceramics International · 2022-07-31

    Bin Liu, Yaoyao Mo, Yongwei Liu, Yunjun Lu, Xiaoyan He, Yinsheng Xu, Andrey A. Lipovskii, Guang Yang

    Read original paper ↗ ·

  36. Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy

    Chemistry - An Asian Journal · 2022-09-11

    Xiangdong Wang, Tian Lu, Wenyan Zhou, Xiaobo Ji, Wencong Lu, Jiong Yang

    Read original paper ↗ ·

  37. Identification and ranking biomaterials for bone scaffolds using machine learning and PROMETHEE

    Research on Biomedical Engineering · 2023-02-18

    Sabah Javaid, Hamed Taheri Gorji, Khaoula Belhaj Soulami, Naima Kaabouch

    Read original paper ↗ ·

  38. Classification and prediction model of ancient glass based on AP-CART

    Unknown journal · 2023-08-23

    Juanjuan Wang, Wenjian Wu, Yayun Wang, Guangming Shao

    Read original paper ↗ ·

  39. Inverse design of polymer alloys using deep learning based on self-consistent field analysis and finite element analysis

    Materials Today Communications · 2023-10-07

    Kazuya Hiraide, Yutaka Oya, Misato Suzuki, Mayu Muramatsu

    Read original paper ↗ ·

  40. Scope of machine learning in materials research—A review

    Applied Surface Science Advances · 2023-11-28

    Md Hosne Mobarak, Mariam Akter Mimona, Md. Aminul Islam, Nayem Hossain, Fatema Tuz Zohura, Ibnul Imtiaz, Md Israfil Hossain Rimon

    Read original paper ↗ ·

  41. Prediction of Phase Composition and Process Resilience in Plasma‐Assisted Hetero‐Aggregate Synthesis using a Machine‐Learning Model with Multivariate Output

    Advanced Materials Technologies · 2024-02-09

    Yuanqing Lu, Timur R. Fazletdinov, Zhiwen Pan, Katrin Wondraczek, Lothar Wondraczek

    Read original paper ↗ ·

  42. Development of a deep-learning model for phase-separation structure of diblock copolymer based on self-consistent field analysis

    Advanced Composite Materials · 2024-02-24

    Kazuya Hiraide, Yutaka Oya, Kenta Hirayama, Katsuhiro Endo, Mayu Muramatsu

    Read original paper ↗ ·

  43. Highly effective design of high GFA alloys with different metal-based and various components by machine learning

    Science China Technological Sciences · 2024-04-23

    Yichuan Tang, Yifan He, ZhuoQun Fan, Zhongqi Wang, Chengying Tang

    Read original paper ↗ ·

  44. Machine learning-accelerated discovery and design of electrode materials and electrolytes for lithium ion batteries

    Energy storage materials · 2024-08-13

    Guangsheng Xu, Mingxi Jiang, Jinliang Li, Jinliang Li, Xiaoyang Xuan, Jiabao Li, Jiabao Li, Ting Lu, Likun Pan

    Read original paper ↗ ·

  45. Deep learning-aided inverse analysis framework to accelerate the exploration of DP steel microstructures

    Materials Today Communications · 2024-09-30

    Misato Suzuki, Kazuyuki SHIZAWA, Mayu Muramatsu

    Read original paper ↗ ·

  46. Beyond the Surface: Interconnection of Viscosity, Crystal Growth, and Diffusion in Ge25Se75 Glass-Former

    The Journal of Physical Chemistry B · 2024-10-07

    Jaroslav Barták, David Vaculík, Michaela Včeláková, Simona Martinková, Torsten Wieduwilt, Markus Alexander Schmidt, Michal Kurka, Stanislav Šlang, Karel Pálka, Petr Košťál, Petr Bělina, Pavla Honcová, Jiřı́ Málek

    Read original paper ↗ ·

  47. 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

    Read original paper ↗ ·

  48. Enhancing Copper Interconnect Reliability Based on Machine Learning: Electromigration Lifetime Prediction Via an -Interpretable Qpso-Xgboost Hybrid Model

    SSRN Electronic Journal · 2025-01-01

    Kai Wei Sun, MingYang Zuo, TianBo Wu, Li Tian, Ming Wang, J. Sun

    Read original paper ↗ ·

  49. Machine Learning Based Prediction of Creep Life for Nickel-Based Single Crystal Superalloys

    Computers, materials & continua/Computers, materials & continua (Print) · 2025-01-01

    Lijie Wang, Xuguang Dong, Yao Lu, Xiaoming Du, Jide Liu

    Read original paper ↗ ·

  50. Optimized batch identification of metallic alloy materials for precision machining operations using machine learning and image processing techniques

    Matéria (Rio de Janeiro) · 2025-01-01

    Indra Pandian, Nuha Alruwais, Haya Mesfer Alshahrani, Shyamala Chandrasekaran

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