BetaCitation Atlas

RESEARCH PAPER · Zhongguo kexue. Wulixue Lixue Tianwenxue · 2020

Application of machine learning approach in disordered materials

JiaQi WU, Yitao Sun, Weihua Wang, Maozhi Li

Read original paper ↗

See where this research leads.

7 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

Application of machine learning approach in disordered materialsOriginalAdvantages and Disadvantages of Various Uncertainty Assessment Methods in Material and Technological Predictive Models2Identification of atomic rearrangements in amorphous alloys based on machine learning7Prediction of Phase Composition and Process Resilience in Plasma‐Assisted Hetero‐Aggregate Synthesis using a Machine‐Learning Model with Multivariate Output7Glass forming ability prediction of bulk metallic glasses based on fused strategy5Application of Machine Learning in Amorphous Alloys4Cryogenic static and dynamic deformation behavior of Zr-based bulk metallic glasses8Quantitatively predicting the composition-structure-properties in laser glasses: Fundamental theory and practice0

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

7 direct citing papers saved.

  1. Advantages and Disadvantages of Various Uncertainty Assessment Methods in Material and Technological Predictive Models

    Unknown journal · 2021-01-01

    Jacek Pietraszek, M. Krawczyk, Andrzej Kiełbus

    Read original paper ↗ ·

  2. Identification of atomic rearrangements in amorphous alloys based on machine learning

    Journal of Materials Research and Technology · 2023-11-01

    Yiying Xu, Shidong Feng, Xiaoqian Lu, Li‐Min Wang

    Read original paper ↗ ·

  3. 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 ↗ ·

  4. Glass forming ability prediction of bulk metallic glasses based on fused strategy

    Transactions of Nonferrous Metals Society of China · 2024-05-01

    Ting ZHANG, Zhilin Long, Li PENG

    Read original paper ↗ ·

  5. 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 ↗ ·

  6. Cryogenic static and dynamic deformation behavior of Zr-based bulk metallic glasses

    Intermetallics · 2025-05-24

    Zhenxiang Zhao, Chunyan Li, Tianyu Chen, Jianhui Liu, Xiaoqiang Fu, Shengzhong Kou, Xiaocheng Li, Yudian Ouyang, Jinfeng Kang

    Read original paper ↗ ·

  7. Quantitatively predicting the composition-structure-properties in laser glasses: Fundamental theory and practice

    Zhongguo kexue. Wulixue Lixue Tianwenxue · 2025-12-04

    Shuangli Dong, Yao Tang Ji, Xinyu Liu, Qinyuan Zhang, Zhonghong Jiang

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