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RESEARCH PAPER · Journal of Chemical Information and Modeling · 2021

Leveraging Transfer Learning and Chemical Principles toward Interpretable Materials Properties

Liyuan Huang, Chen Ling

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

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Leveraging Transfer Learning and Chemical Principles toward Interpretable Materials PropertiesOriginalDensity of states prediction for materials discovery via contrastive learning from probabilistic embeddings91A review of the recent progress in battery informatics178Not from Scratch: Predicting Thermophysical Properties through Model-Based Transfer Learning Using Graph Convolutional Networks5Machine learning reaction barriers in low data regimes: a horizontal and diagonal transfer learning approach24Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence43Transfer learning aided high-throughput computational design of oxygen evolution reaction catalysts in acid conditions28Computer vision enabled high-quality electrochemical experimentation3Tailor-made adsorbent materials on machine learning instructions for efficient purification of heavy metal contaminants in wastewater1Physics-informed machine learning for composition, processing, microstructure and performance linkages in high-entropy alloy coatings: A review1

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  1. Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings

    Nature Communications · 2022-02-17

    Shufeng Kong, Francesco Ricci, Dan Guevarra, Jeffrey B. Neaton, Carla Pedro Gomes, John M. Gregoire

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  2. A review of the recent progress in battery informatics

    npj Computational Materials · 2022-02-18

    Chen Ling

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  3. Not from Scratch: Predicting Thermophysical Properties through Model-Based Transfer Learning Using Graph Convolutional Networks

    Journal of Chemical Information and Modeling · 2022-10-31

    Rodrigo Hormázabal, Jeong Won Kang, Kiho Park, Dae Ryook Yang

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  4. Machine learning reaction barriers in low data regimes: a horizontal and diagonal transfer learning approach

    Digital Discovery · 2023-01-01

    Samuel G. Espley, Elliot H. E. Farrar, David Buttar, Simone Tomasi, Matthew N. Grayson

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  5. Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence

    Expert Opinion on Drug Delivery · 2023-01-16

    Jonathan Zaslavsky, Pauric Bannigan, Christine J. Allen

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  6. Transfer learning aided high-throughput computational design of oxygen evolution reaction catalysts in acid conditions

    Journal of Energy Chemistry · 2023-02-13

    Siwen Wang, Honghong Lin, Yui Wakabayashi, Li Qin Zhou, Charles A. Roberts, Debasish Banerjee, Hongfei Jia, Chen Ling

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  7. Computer vision enabled high-quality electrochemical experimentation

    Digital Discovery · 2024-01-01

    Keiichi Okubo, Jaydeep Thik, Tomoya Yamaguchi, Chen Ling

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  8. Tailor-made adsorbent materials on machine learning instructions for efficient purification of heavy metal contaminants in wastewater

    Environmental Functional Materials · 2025-12-09

    Jinwen Zeng, Minjie Qin, Mingzhe Jiang, Shaochi Wang, Xinyue Xie, Lijia Li, Wei Ren, Penghui Shao, Xiao Xiao, Xubiao Luo

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  9. Physics-informed machine learning for composition, processing, microstructure and performance linkages in high-entropy alloy coatings: A review

    Journal of Alloys and Compounds · 2026-06-22

    Bin Miao, Guan-Qi Lin, XU Yuan-xun

    Read original paper ↗ ·
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