BetaCitation Atlas

RESEARCH PAPER · Applied Physics Letters · 2023

Rapid acquisition of liquid thermophysical properties from pure metals to quaternary alloys by proposing a machine learning strategy

Read original paper ↗

See where this research leads.

8 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

Rapid acquisition of liquid thermophysical properties from pure metals to quaternary alloys by proposing a machine learning strategyOriginalActive learning prediction and experimental confirmation of atomic structure and thermophysical properties for liquid Hf 76 W 24 refractory alloy8Tutorial: Deep learning prediction of thermophysical properties for liquid multicomponent alloys10A deep neural network potential model for theoretically predicting thermal transport, mechanical properties of multi-layered graphitic carbon nitride with molecular dynamics12Active learning prediction and electromagnetic levitation measurement of metastable liquid properties of Ti-Al-Zr alloy1Application of Deep-Learning Potential in Simulating the Structural and Physical Characteristics of Platinum1Alloy microstructure segmentation through SAM and domain knowledge without extra training7Machine learning in conductivity development of specific alloys1Liquid structure dependence of rapid crystallization dynamics for rare-earth terbium0

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

Browse Matching Papers

Papers that cite this work

8 direct citing papers saved.

  1. Active learning prediction and experimental confirmation of atomic structure and thermophysical properties for liquid Hf 76 W 24 refractory alloy

    Physical review. E · 2023-11-21

    Kelun Liu, Ruilin Xiao, Ying Ruan, Bingbo Wei

    Read original paper ↗ ·
  2. Tutorial: Deep learning prediction of thermophysical properties for liquid multicomponent alloys

    Journal of Applied Physics · 2023-11-21

    Ruilin Xiao, Kelun Liu, Ying Ruan, Liang Hu, Bingbo Wei

    Read original paper ↗ ·
  3. A deep neural network potential model for theoretically predicting thermal transport, mechanical properties of multi-layered graphitic carbon nitride with molecular dynamics

    International Communications in Heat and Mass Transfer · 2024-11-26

    Hongxia Li, Lang Wu, Changshun Xia, Shuiqing Huang, Meiqin Ni, Chunlin Huang, Ming Xu, Zhaohui Ruan

    Read original paper ↗ ·
  4. Active learning prediction and electromagnetic levitation measurement of metastable liquid properties of Ti-Al-Zr alloy

    Review of Materials Research · 2025-01-01

    Ruilin Xiao, Kelun Liu, H.R. Li, Ying Ruan

    Read original paper ↗ ·
  5. Application of Deep-Learning Potential in Simulating the Structural and Physical Characteristics of Platinum

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

    Keyuan Chen, Xingkao Zhang, Li Ma, Jueyi Ye, Qi Qiu, Haoxiang Zhang, Ju Rong, Yudong Sui, Xiaohua Yu, Jing Feng

    Read original paper ↗ ·
  6. Alloy microstructure segmentation through SAM and domain knowledge without extra training

    Scripta Materialia · 2025-02-01

    Xudong Ma, Yuqi Zhang, Chenchong Wang, Wei Xu

    Read original paper ↗ ·
  7. Machine learning in conductivity development of specific alloys

    Canadian Metallurgical Quarterly · 2026-02-27

    Omid Karimi Sadaghiani, Sahba Tabarraei, İlknur Tunç, Wei Peng, Raha Shabani

    Read original paper ↗ ·
  8. Liquid structure dependence of rapid crystallization dynamics for rare-earth terbium

    Applied Physics Letters · 2026-06-01

    Yu Zhang, D. L. Geng, Y.J. Jin, Bingbo Wei

    Read original paper ↗ ·