BetaCitation Atlas

RESEARCH PAPER · npj Computational Materials · 2021

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

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

Read original paper ↗

See where this research leads.

93 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

A general and transferable deep learning framework for predicting phase formation in materialsOriginalBoosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques32Application of deep transfer learning to predicting crystal structures of inorganic substances33Machine learning prediction of magnetic properties of Fe-based metallic glasses considering glass forming ability75A highly interpretable materials informatics approach for predicting microstructure-property relationship in fabric composites29Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data198Prediction of Ti-Zr-Nb-Ta high-entropy alloys with desirable hardness by combining machine learning and experimental data48Internal Crack Prediction of Continuous Casting Billet Based on Principal Component Analysis and Deep Neural Network27Efficient alloy design of Sr-modified A356 alloys driven by computational thermodynamics and machine learning73A critical review of the machine learning guided design of metallic glasses for superior glass-forming ability13Algorithmic Screening Frontiers: How Model Priors Reshape Searchable Materials Space0Prediction without Transferability: Domain Shift in Cross-Material AI Inference0Deep-Learning-Assisted Focused Ion Beam Nanofabrication41Accurate energy prediction of large-scale defective two-dimensional materials via deep learning6On the use of transfer modeling to design new steels with excellent rotating bending fatigue resistance even in the case of very small calibration datasets78Numerical simulation of dendritic growth during solidification process using multiphase-field model aided with machine learning method11A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys33Next-generation energy systems for sustainable smart cities: Roles of transfer learning118State-of-the-Art Review of Machine Learning Applications in Additive Manufacturing; from Design to Manufacturing and Property Control44Deep and transfer learning for building occupancy detection: A review and comparative analysis123Machine Learning-Assisted Identification of Copolymer Microstructures Based on Microscopic Images22Predicting single-phase solid solutions in as-sputtered high entropy alloys: High-throughput screening with machine-learning model54A Knowledge Transfer Framework for General Alloy Materials Properties Prediction9Predict the phase formation of high-entropy alloys by compositions101A Conceptual Distinction between Generalization and Transfer in Materials Machine Learning0Algorithmic Novelty vs Chemical Novelty: Rethinking Innovation Metrics0An Interactive and Iterative Framework for Refractory Alloys Based on Machine Learning, Calphad and Experiments0Cross-Property Entanglement in Multi-Task Materials Learning Systems0Developing efficient deep learning model for predicting copolymer properties6Domain knowledge aided machine learning method for properties prediction of soft magnetic metallic glasses32Influence of Al and Ti Alloying and Annealing on the Microstructure and Compressive Properties of Cr-Fe-Ni Medium-Entropy Alloy0Influence of Al and Ti Alloying and Annealing on the Microstructure and Compressive Properties of Cr-Fe-Ni Medium-Entropy Alloy0Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films14The Interpretability–Complexity Paradox in Deep Materials Networks0Transfer Learning in Inorganic Compounds’ Crystal Structure Classification1Latest Advances in Manufacturing and Machine Learning of Bulk Metallic Glasses14A two-step data augmentation method based on generative adversarial network for hardness prediction of high entropy alloy51Prediction of phase selection of amorphous alloys and high entropy alloys by artificial neural network19Advances in machine learning- and artificial intelligence-assisted material design of steels109Representations of Materials for Machine Learning133Pre-Activation based Representation Learning to Enhance Predictive Analytics on Small Materials Data7Materials genome strategy for metallic glasses19An end-to-end machine learning framework exploring phase formation for high entropy alloys15Exploiting the use of deep learning techniques to identify phase separation in self-assembled microstructures with localized graphene domains in epoxy blends5Hardness prediction of high entropy alloys with periodic table representation of composition, processing, structure and physical parameters15On the application of high‐throughput experimentation and data‐driven approaches in metallic glasses19Investigating representation schemes for surrogate modeling of High Entropy Alloys8Relating the combinatorial materials chip mapping to the glass-forming ability of bulk metallic glasses via diffraction peak width9Advances of machine learning in materials science: Ideas and techniques105Design and Validation of Refractory Alloys Using Machine Learning, Calphad, and Experiments21Development and application of Few-shot learning methods in materials science under data scarcity47Transfer Learning Across Materials Classes: Conceptual Boundaries of Reusability0Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets90Deep learning-based prediction of saturation magnetic flux density in Fe-based metallic glasses via transfer learning5Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO 3 -type perovskite with doping modification8Machine learning-enabled prediction of high-temperature oxidation resistance for Ni-based alloys29Addressing the Accuracy-Cost Trade-off in Material Property Prediction Using a Teacher-Student Strategy5Comparative Study on Different CNN Architectures Developed on Microstructural Classification in Al-Si Alloys3Machine learning studies for magnetic compositionally complex alloys: A critical review33Machine-learning-assisted analysis of highly transient X-ray imaging sequences of weld pools6A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning18LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns10Influence of Al and Ti Alloying and Annealing on the Microstructure and Compressive Properties of Cr-Fe-Ni Multi-Principal Element Alloy4Data-driven inverse design of MoNbTiVWZr refractory multicomponent alloys: Microstructure and mechanical properties7Intelligent Nanomaterial Image Characterizations – A Comprehensive Review on AI Techniques that Power the Present and Drive the Future of Nanoscience17Advances in smart cities with system integration and energy digitalization technologies: A state-of-the-art review21A Multi-Fidelity Learning Framework for High-Entropy Alloy Design with Sparse DFT and Abundant CALPHAD Data0Applications of machine learning in high-entropy alloys: a review of recent advances in design, discovery, and characterization41Cluster expansion augmented transfer learning for property prediction of high-entropy alloys6Harnessing the leading edge: machine learning ventures in chemistry and materials science3Machine Learning Algorithms for Applications in Materials Science I2Toward super-clean bearing steel by a novel physical-data integrated design strategy3Enhancing material property prediction via a dual stream model fusing spatial and topological information2A catalogue of metallic glass-forming alloy systems5Machine Learning-Based Computational Design Methods for High-Entropy Alloys49Application of Machine Learning in Amorphous Alloys4Design of BCC/FCC dual-solid solution refractory high-entropy alloys through CALPHAD, machine learning and experimental methods32Metallic glass roadmap26Multi-attribute-physical-law-informed transfer learning to design high-entropy Elinvar alloys4Machine Learning Inverse Prediction of Cu-Zn-Al Alloy’s Composition and Target Properties Using a Two-Way Feedback Structure on a Small Data Set1Phase classification of high entropy alloys with composition, common physical, elemental-property descriptors and periodic table representation2Composition Design and Property Prediction for AlCoCrCuFeNi High-Entropy Alloy Based on Machine Learning4Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements10Computation- and process-based design for advanced structural high-entropy alloy development and analyses: A critical review32Probing dendrite growth under microgravity via machine learning-aided multi-scale characterisation2A deep recurrent learning framework for predicting saturation magnetic flux density in Fe-based metallic glasses1Theoretical investigations on the activation of Silanes by rare-earth complexes: Mechanisms, bonding, and catalytic implications0Towards intelligent design of metallic glasses: a data-driven pathway for closing the theory-experiment loop1A Comprehensive Review of the Computational Approaches for Structure–Property Prediction of High Entropy Materials0Integrated machine-learning modelling for mechanical property prediction – A case study on laser-welded TC4 titanium alloy4From empirical rules to data-driven design of Al-based amorphous alloys0Mechanical property prediction of superalloys with microporosity defects using a multi-source deep learning framework0A novel feature engineering method for high-performance and interpretable machine learning prediction of fatigue life in high-strength steel weld metal0Leveraging large language models to address data scarcity in machine learning for graphene synthesis1

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

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

  1. 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 ↗ ·

  2. Application of deep transfer learning to predicting crystal structures of inorganic substances

    Computational Materials Science · 2021-04-13

    Shuo Feng, Huiyu Zhou, Hongbiao Dong

    Read original paper ↗ ·

  3. 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 ↗ ·

  4. A highly interpretable materials informatics approach for predicting microstructure-property relationship in fabric composites

    Composites Science and Technology · 2021-10-04

    Tina Olfatbakhsh, Abbas S. Milani

    Read original paper ↗ ·

  5. Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

    Nature Communications · 2021-11-15

    Vishu Gupta, Kamal Kumar Choudhary, Francesca Tavazza, Carelyn E. Campbell, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal

    Read original paper ↗ ·

  6. Prediction of Ti-Zr-Nb-Ta high-entropy alloys with desirable hardness by combining machine learning and experimental data

    Applied Physics Letters · 2021-11-15

    Yan Hui Sun, Zhichao Lu, Zhichao Lu, Xiongjun Liu, Qing Du, Huamin Xie, Jiecheng Lv, Ruoxuan Song, Yuan Wu, Hui Wang, Suihe Jiang, Zhaoping Lu, Zhaoping Lu

    Read original paper ↗ ·

  7. Internal Crack Prediction of Continuous Casting Billet Based on Principal Component Analysis and Deep Neural Network

    Metals · 2021-12-08

    Leilei Zou, Jiangshan Zhang, Yanshen Han, Fanzheng Zeng, Quanhui Li, Qing Liu

    Read original paper ↗ ·

  8. Efficient alloy design of Sr-modified A356 alloys driven by computational thermodynamics and machine learning

    Journal of Material Science and Technology · 2021-12-26

    Yi Wang, Guangchen Liu, Zhao Lu, Jianbao Gao, Lijun Zhang

    Read original paper ↗ ·

  9. 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 ↗ ·

  10. Algorithmic Screening Frontiers: How Model Priors Reshape Searchable Materials Space

    Journal of Computational and Data-Driven Materials Engineering · 2022-01-01

    Nguyen Thanh Huy, Pham Quang Minh, Le Thi Bich

    Read original paper ↗ ·

  11. Prediction without Transferability: Domain Shift in Cross-Material AI Inference

    Journal of Computational and Data-Driven Materials Engineering · 2022-01-01

    Maria Gonzalez, Javier Ruiz, Lucia Torres

    Read original paper ↗ ·

  12. Deep-Learning-Assisted Focused Ion Beam Nanofabrication

    Nano Letters · 2022-03-24

    Oleksandr Buchnev, James A. Grant‐Jacob, Robert W. Eason, Nikolay I. Zheludev, Ben Mills, Kevin F. MacDonald

    Read original paper ↗ ·

  13. Accurate energy prediction of large-scale defective two-dimensional materials via deep learning

    Applied Physics Letters · 2022-05-23

    Yuxing Ma, Shuaihua Lu, Yehui Zhang, Tingbo Zhang, Qionghua Zhou, Jinlan Wang

    Read original paper ↗ ·

  14. On the use of transfer modeling to design new steels with excellent rotating bending fatigue resistance even in the case of very small calibration datasets

    Acta Materialia · 2022-06-17

    Xiaolu Wei, Sybrand van der Zwaag, Zixi Jia, Chenchong Wang, Wei Xu

    Read original paper ↗ ·

  15. Numerical simulation of dendritic growth during solidification process using multiphase-field model aided with machine learning method

    Calphad · 2022-06-27

    Liangyuan Ren, Shaoning Geng, Ping Jiang, Song Gao, Chu Han

    Read original paper ↗ ·

  16. A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys

    Scientific Reports · 2022-07-08

    Kyungtae Lee, Mukil V. Ayyasamy, Yangfeng Ji, Prasanna V. Balachandran

    Read original paper ↗ ·

  17. Next-generation energy systems for sustainable smart cities: Roles of transfer learning

    Sustainable Cities and Society · 2022-07-19

    Yassine Himeur, Mariam Elnour, Fodil Fadli, Nader Meskin, Ioan Petri, Yacine Rezgui, Fayçal Bensaali, Abbes Amira

    Read original paper ↗ ·

  18. State-of-the-Art Review of Machine Learning Applications in Additive Manufacturing; from Design to Manufacturing and Property Control

    Archives of Computational Methods in Engineering · 2022-07-22

    Garshasp Keyvan Sarkon, Babak Safaei, Mohammad Saleh Kenevisi, Samaneh Arman, Qasim Zeeshan

    Read original paper ↗ ·

  19. Deep and transfer learning for building occupancy detection: A review and comparative analysis

    Engineering Applications of Artificial Intelligence · 2022-08-02

    Aya Nabil Sayed, Yassine Himeur, Fayçal Bensaali

    Read original paper ↗ ·

  20. Machine Learning-Assisted Identification of Copolymer Microstructures Based on Microscopic Images

    ACS Applied Materials & Interfaces · 2022-10-07

    Han Xu, Sainan Ma, Yang Hou, Qinghua Zhang, Rui Wang, Yingwu Luo, Xiang Yang Gao

    Read original paper ↗ ·

  21. Predicting single-phase solid solutions in as-sputtered high entropy alloys: High-throughput screening with machine-learning model

    Journal of Material Science and Technology · 2022-10-17

    Ji‐Chang Ren, Junjun Zhou, Christopher J. Butch, Zhigang Ding, Shuang long Li, Yonghao Zhao, Wei Liu

    Read original paper ↗ ·

  22. A Knowledge Transfer Framework for General Alloy Materials Properties Prediction

    Materials · 2022-10-24

    Hang Sun, Heye Zhang, Guangli Ren, Chao Zhang

    Read original paper ↗ ·

  23. Predict the phase formation of high-entropy alloys by compositions

    Journal of Materials Research and Technology · 2022-12-29

    Qingwei Guo, Xiaotao Xu, Xiaolong Pei, Zhiqiang Duan, Peter Kaehuei Liaw, Hua Hou, Yuhong Zhao

    Read original paper ↗ ·

  24. A Conceptual Distinction between Generalization and Transfer in Materials Machine Learning

    Journal of Artificial Intelligence for Materials Science · 2023-01-01

    中村博志, Yuta Kato

    Read original paper ↗ ·

  25. Algorithmic Novelty vs Chemical Novelty: Rethinking Innovation Metrics

    Journal of Computational and Data-Driven Materials Engineering · 2023-01-01

    Lucas Andrade, Mariana Lopes

    Read original paper ↗ ·

  26. An Interactive and Iterative Framework for Refractory Alloys Based on Machine Learning, Calphad and Experiments

    SSRN Electronic Journal · 2023-01-01

    Wenjie Li, Lavanya Raman, Marcia Ahn, Arindam Debnath, Shuang Lin, Adam M. Krajewski, Shun‐Li Shang, Shashank Priya, Wesley F. Reinhart, Zi‐Kui Liu, Allison M. Beese

    Read original paper ↗ ·

  27. Cross-Property Entanglement in Multi-Task Materials Learning Systems

    Journal of Computational and Data-Driven Materials Engineering · 2023-01-01

    Daniel Fischer, Laura Meier, Thomas Braun, Stefan Koch

    Read original paper ↗ ·

  28. Developing efficient deep learning model for predicting copolymer properties

    Physical Chemistry Chemical Physics · 2023-01-01

    Himanshu ., Kaushik Chakraborty, Tarak K. Patra

    Read original paper ↗ ·

  29. Domain knowledge aided machine learning method for properties prediction of soft magnetic metallic glasses

    Transactions of Nonferrous Metals Society of China · 2023-01-01

    Xin Li, Guangcun Shan, Hongbin Zhao, C.H. Shek

    Read original paper ↗ ·

  30. Influence of Al and Ti Alloying and Annealing on the Microstructure and Compressive Properties of Cr-Fe-Ni Medium-Entropy Alloy

    SSRN Electronic Journal · 2023-01-01

    Tailin Yang, Junjie Feng, Yanyan Wang, Honglian Deng, Xiang Zhang, Qingkun Meng, Jiqiu Qi, Fuxiang Wei, Zhenghai Hu, Dongmei Meng, Yanwei Sui

    Read original paper ↗ ·

  31. Influence of Al and Ti Alloying and Annealing on the Microstructure and Compressive Properties of Cr-Fe-Ni Medium-Entropy Alloy

    SSRN Electronic Journal · 2023-01-01

    Tailin Yang, Junjie Feng, Yanyan Wang, Honglian Deng, Xiang Zhang, Qingkun Meng, Jiqiu Qi, Fuxiang Wei, Zhenghai Hu, Dongmei Meng, Yanwei Sui

    Read original paper ↗ ·

  32. Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films

    Digital Discovery · 2023-01-01

    Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj, Yuanhang Cheng, Daniil Bash, Felipe Oviedo, J. Senthilnath, Vijila Chellappan, Yee‐Fun Lim, Armin Gerhard Aberle, Benjamin P. MacLeod, Fraser G. L. Parlane, Curtis P. Berlinguette, Qianxiao Li, Tonio Buonassisi, Zhe Liu

    Read original paper ↗ ·

  33. The Interpretability–Complexity Paradox in Deep Materials Networks

    Journal of Computational and Data-Driven Materials Engineering · 2023-01-01

    Bruno Martins, Lucas Pereira, Renata Azevedo

    Read original paper ↗ ·

  34. Transfer Learning in Inorganic Compounds’ Crystal Structure Classification

    Crystals · 2023-01-02

    Hanan A. Hosni Mahmoud

    Read original paper ↗ ·

  35. 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 ↗ ·

  36. A two-step data augmentation method based on generative adversarial network for hardness prediction of high entropy alloy

    Computational Materials Science · 2023-02-07

    Zhiyuan Yang, Shu Li, Shuai Li, Jia Yang, Dong-Rong Liu

    Read original paper ↗ ·

  37. Prediction of phase selection of amorphous alloys and high entropy alloys by artificial neural network

    Computational Materials Science · 2023-03-16

    Lin Wang, Peiyou Li, Wei Zhang, Fangyi Wan, Junxia Wu, Longquan Yong, Xiaodi Liu

    Read original paper ↗ ·

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

    International Journal of Minerals Metallurgy and Materials · 2023-04-18

    Guangfei Pan, Feiyang Wang, Chunlei Shang, Hong‐Hui Wu, Guilin Wu, Junheng Gao, Shuize Wang, Zhijun Gao, Xiaoye Zhou, Xinping Mao

    Read original paper ↗ ·

  39. Representations of Materials for Machine Learning

    Annual Review of Materials Research · 2023-04-18

    James R. Jr. Damewood, Jessica Karaguesian, Jaclyn R. Lunger, Aik Rui Tan, Mingrou Xie, Jiayu Peng, Rafael Gómez‐Bombarelli

    Read original paper ↗ ·

  40. Pre-Activation based Representation Learning to Enhance Predictive Analytics on Small Materials Data

    Unknown journal · 2023-06-18

    Vishu Gupta, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal

    Read original paper ↗ ·

  41. 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 ↗ ·

  42. An end-to-end machine learning framework exploring phase formation for high entropy alloys

    Transactions of Nonferrous Metals Society of China · 2023-07-01

    Huiran Zhang, Rui Qin Hu, Xi Liu, Shengzhou Li, Guang-jie ZHANG, Quan Qian, Guangtai Ding, Dongbo Dai

    Read original paper ↗ ·

  43. Exploiting the use of deep learning techniques to identify phase separation in self-assembled microstructures with localized graphene domains in epoxy blends

    Computational Materials Science · 2023-07-21

    Anuradha Kamble, Suihua He, Jonathan R. Howse, Carwyn Ward, Ian Hamerton

    Read original paper ↗ ·

  44. Hardness prediction of high entropy alloys with periodic table representation of composition, processing, structure and physical parameters

    Journal of Alloys and Compounds · 2023-08-14

    Shuai Li, Shuai Li, Shu Li, Jia Yang, Mingyu Zhang

    Read original paper ↗ ·

  45. On the application of high‐throughput experimentation and data‐driven approaches in metallic glasses

    Materials Genome Engineering Advances · 2023-09-01

    Weijie Xie, Weihua Wang, Yanhui Liu

    Read original paper ↗ ·

  46. Investigating representation schemes for surrogate modeling of High Entropy Alloys

    Computational Materials Science · 2023-09-05

    Arindam Debnath, Wesley F. Reinhart

    Read original paper ↗ ·

  47. Relating the combinatorial materials chip mapping to the glass-forming ability of bulk metallic glasses via diffraction peak width

    Scripta Materialia · 2023-11-01

    Ze Liu, Cai Chen, Yuanxun Zhou, Lanting T. Zhang, Hong Wang

    Read original paper ↗ ·

  48. Advances of machine learning in materials science: Ideas and techniques

    Frontiers of Physics · 2023-11-24

    Sue Sin Chong, Yi Sheng Ng, Hui‐Qiong Wang, Jin‐Cheng Zheng

    Read original paper ↗ ·

  49. Design and Validation of Refractory Alloys Using Machine Learning, Calphad, and Experiments

    International Journal of Refractory Metals and Hard Materials · 2024-01-01

    Wenjie Li, Lavanya Raman, Arindam Debnath, Marcia Ahn, Shuang Lin, Adam M. Krajewski, Shun‐Li Shang, Shashank Priya, Wesley F. Reinhart, Zi‐Kui Liu, Allison M. Beese

    Read original paper ↗ ·

  50. Development and application of Few-shot learning methods in materials science under data scarcity

    Journal of Materials Chemistry A · 2024-01-01

    Yongxing Chen, Long Peng, Bin Liu, Yi Wang, Junlong Wang, Tian Ma, Huilin Wei, Yue Kang, Haining Ji

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