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RESEARCH PAPER · Computational Materials Science · 2021

Graph-based deep learning frameworks for molecules and solid-state materials

Weiyi Gong, Qimin Yan

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Graph-based deep learning frameworks for molecules and solid-state materialsOriginalGraphene oxide enhanced the endocrine disrupting effects of bisphenol A in adult male zebrafish: Integrated deep learning and metabolomics studies27Discovery without Understanding: A Systems Theory of Black-Box Optimization in Autonomous Materials Engineering0Latent Space Collapse in Materials Representation Learning: A Systems-Level Conceptual Analysis0Representation Learning in Materials Science: Architectures, Data Modalities, and Discovery Applications03D-structure-attention graph neural network for crystals and materials10A Generative Approach to Materials Discovery, Design, and Optimization121Graph attention network for global search of atomic clusters: A case study of Agn (n = 14−26) clusters20Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges66Molecular geometric deep learning32Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance26Explainable AI for Interpretable Structure–Property Maps in Polymer Blend Systems0Substitutional alloying using crystal graph neural networks3GNNs for mechanical properties prediction of strut-based lattice structures46Drug Solubility Prediction: A Comparative Analysis of GNN, MLP, and Traditional Machine Learning Algorithms6Methods and applications of machine learning in computational design of optoelectronic semiconductors22Graph representation of multi-dimensional materials2Leveraging Graph Neural Networks for Enhanced Prediction of Molecular Solubility via Transfer Learning2Opportunities and challenges of graph neural networks in electrical engineering27Graph pooling in graph neural networks: methods and their applications in omics studies23GenAI for Scientific Discovery in Electrochemical Energy Storage: State‐of‐the‐Art and Perspectives from Nano‐ and Micro‐Scale11Deep learning in-depth analysis of crystal graph convolutional neural networks: A new era in materials discovery and its applications9Model Collapse in Materials AI: A Conceptual Account of Over-Reuse, Dataset Recycling, and Knowledge Decay0New phases of 2D group-VA nanostructures with unusual auxetic mechanical properties induced by the regularity of the interatomic interaction force1Polymer Property Prediction Using Machine Learning4A comprehensive update on the use of molecular topology applications for anti-infective drug discovery5Exploring the microstructure–property relationship in polymer foams using advanced statistical methods, machine learning and deep learning: A review9Toward Stable Zinc Anode: An AI‐Assisted High‐Throughput Screening of Electrolyte Additives for Aqueous Zinc‐Ion Battery42Toward Stable Zinc Anode: An AI‐Assisted High‐Throughput Screening of Electrolyte Additives for Aqueous Zinc‐Ion Battery4Ensembles of Graph and Physics-Informed Machine Learning for Scientific Modeling in Materials Science: A Review11A Self-Supervised Graph Attention Network for Accelerating the Discovery of High-Conductivity Solid-State Electrolytes0AI‐Driven Decoding of Material Dynamics: From Machine Learning Potentials and Interpretability to Generative Prediction18AI-driven design and applications of quantum dots9Graph representation learning for predicting processing conditions in Spinodal decomposition microstructures1Counting Spanning Trees with One Perfect Matching in Helicene Polygonal Chains on the Plane and Cylinder0Twin cross contrastive learning with multi-modality fusion for drug–target affinity prediction0Deep Learning for Anticancer Drug Discovery Targeting Non-Apoptotic Regulated Cell Death Mechanisms0ALiBERT: Transformer-Based NLP and Hybrid Ensemble Learning Framework for Predicting Material Properties0

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37 direct citing papers saved.

  1. Graphene oxide enhanced the endocrine disrupting effects of bisphenol A in adult male zebrafish: Integrated deep learning and metabolomics studies

    The Science of The Total Environment · 2021-10-29

    Pengyu Chen, Jing Yang, Ruihan Wang, Bowen Xiao, Qing Liu, Binbin Sun, Xiaolei Wang, Lingyan Y. Zhu

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  2. Discovery without Understanding: A Systems Theory of Black-Box Optimization in Autonomous Materials Engineering

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

    Daniel Brooks, Amelia Carter, Ethan Moore

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  3. Latent Space Collapse in Materials Representation Learning: A Systems-Level Conceptual Analysis

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

    Hiroshi Nakamura, Yuta Kato

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  4. Representation Learning in Materials Science: Architectures, Data Modalities, and Discovery Applications

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

    Claire Martin, Julien Martin, Sophie Bernard

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  5. 3D-structure-attention graph neural network for crystals and materials

    Molecular Physics · 2022-05-17

    Xuanjie Lin, Hantong Jiang, Liquan Wang, Yongsheng Ren, Wenhui Ma, Shu Zhan

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  6. A Generative Approach to Materials Discovery, Design, and Optimization

    ACS Omega · 2022-07-24

    Dhruv Menon, Raghavan Ranganathan

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  7. Graph attention network for global search of atomic clusters: A case study of Agn (n = 14−26) clusters

    Frontiers of Physics · 2022-11-05

    Linwei Sai, Li Xia Fu, Qiuying Du, Jijun Zhao

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  8. Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges

    Journal of Materials Chemistry A · 2023-01-01

    Swarn Jha, Matthew Yen, Yazmin Soto Salinas, Evan Palmer, John Anzel Villafuerte, Hong Yu Liang

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  9. Molecular geometric deep learning

    Cell Reports Methods · 2023-10-23

    Cong Shen, Jiawei Luo, Kelin Xia

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  10. Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance

    Journal of Chemical Information and Modeling · 2023-11-10

    Vidushi Sharma, Maxwell J. Giammona, Dmitry Yu. Zubarev, Andy Tek, Khanh Nugyuen, Linda K. Sundberg, Daniele Congiu, Young‐Hye La

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  11. Explainable AI for Interpretable Structure–Property Maps in Polymer Blend Systems

    Journal of Artificial Intelligence for Materials Science · 2024-01-01

    Claire Martin, Julien Robert, Sophie Bernard

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  12. Substitutional alloying using crystal graph neural networks

    AIP Advances · 2024-01-01

    Dario Massa, Daniel Cieśliński, Amirhossein D. Naghdi, Stefanos Papanikolaou

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  13. GNNs for mechanical properties prediction of strut-based lattice structures

    International Journal of Mechanical Sciences · 2024-02-02

    Bingyue Jiang, Yangwei Wang, Haiyan Niu, Xingwang Cheng, Pingluo Zhao, Jiawei Bao

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  14. Drug Solubility Prediction: A Comparative Analysis of GNN, MLP, and Traditional Machine Learning Algorithms

    Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji · 2024-03-05

    Veysel Gider, Cafer Budak

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  15. Methods and applications of machine learning in computational design of optoelectronic semiconductors

    Science China Materials · 2024-03-19

    Xiaoyu Yang, Kun Zhou, Xin He, Lijun Zhang

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  16. Graph representation of multi-dimensional materials

    Journal of Physics Materials · 2024-04-01

    Carina T Cai, Amanda J. Parker, Amanda Susan Barnard

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  17. Leveraging Graph Neural Networks for Enhanced Prediction of Molecular Solubility via Transfer Learning

    Technical Education Science/Giáo dục Kỹ thuật · 2024-06-28

    Dat Nguyen, Phuc T. Le

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  18. Opportunities and challenges of graph neural networks in electrical engineering

    Nature Reviews Electrical Engineering · 2024-08-05

    Eli Chien, Mufei Li, Anthony Aportela, Kerr Ding, Shuyi Jia, Supriyo Maji, Zhongyuan Zhao, Javier Mauricio Duarte, Victor Fung, Cong Hao, Yunan Luo, Olgica Milenković, David Zhigang Pan, Santiago Segarra, Pan Li

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  19. Graph pooling in graph neural networks: methods and their applications in omics studies

    Artificial Intelligence Review · 2024-09-16

    Yan Wang, Wenju Hou, Nan Sheng, Ziqi Zhao, Jialin Liu, Lan Huang, Juexin Wang

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  20. GenAI for Scientific Discovery in Electrochemical Energy Storage: State‐of‐the‐Art and Perspectives from Nano‐ and Micro‐Scale

    Small · 2024-10-09

    Shuangqi Li, Fengqi You

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  21. Deep learning in-depth analysis of crystal graph convolutional neural networks: A new era in materials discovery and its applications

    Nanotechnology Reviews · 2025-01-01

    Nilam Qureshi, Jinhong Bang, Jaehyeok Doh

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  22. Model Collapse in Materials AI: A Conceptual Account of Over-Reuse, Dataset Recycling, and Knowledge Decay

    Journal of Artificial Intelligence for Materials Science · 2025-01-01

    Daniel Fischer, Laura Meier, Thomas Braun

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  23. New phases of 2D group-VA nanostructures with unusual auxetic mechanical properties induced by the regularity of the interatomic interaction force

    Nanoscale Advances · 2025-01-01

    Hong Zheng, Zhenhan Zhao, Jiasheng Chen, Hanwen Qin, Jun Li, Xiang Zhao

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  24. Polymer Property Prediction Using Machine Learning

    Challenges and advances in computational chemistry and physics · 2025-01-01

    Vijith Parambil, Ujjwal Tripathi, Hritik Goyal, Rohit Batra

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  25. A comprehensive update on the use of molecular topology applications for anti-infective drug discovery

    Expert Opinion on Drug Discovery · 2025-03-08

    Beatriz Suay‐García, Joan Climent, María Teresa Pérez‐Gracia, Antonio Falcó

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  26. Exploring the microstructure–property relationship in polymer foams using advanced statistical methods, machine learning and deep learning: A review

    Computational Materials Science · 2025-05-15

    Dawid Walicki, Paweł Zawistowski, Joanna Ryszkowska

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  27. Toward Stable Zinc Anode: An AI‐Assisted High‐Throughput Screening of Electrolyte Additives for Aqueous Zinc‐Ion Battery

    Angewandte Chemie International Edition · 2025-08-05

    Guangsheng Xu, Yue Li, Junfeng Li, Junfeng Li, Jinliang Li, Jinliang Li, Xinjuan Liu, Chenglong Wang, Wenjie Mai, Guang Yang, Likun Pan

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  28. Toward Stable Zinc Anode: An AI‐Assisted High‐Throughput Screening of Electrolyte Additives for Aqueous Zinc‐Ion Battery

    Angewandte Chemie · 2025-08-05

    Guangsheng Xu, Yue Li, Junfeng Li, Jinliang Li, Xinjuan Liu, Chenglong Wang, Wenjie Mai, Guang Yang, Likun Pan

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  29. Ensembles of Graph and Physics-Informed Machine Learning for Scientific Modeling in Materials Science: A Review

    Archives of Computational Methods in Engineering · 2025-08-14

    Dennis Delali Kwesi Wayo

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  30. A Self-Supervised Graph Attention Network for Accelerating the Discovery of High-Conductivity Solid-State Electrolytes

    International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI) · 2025-12-05

    Tiantian Gao, Yufeng Wu

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  31. AI‐Driven Decoding of Material Dynamics: From Machine Learning Potentials and Interpretability to Generative Prediction

    Advanced Materials · 2025-12-06

    Long Zhao, Hongxiang Zong

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  32. AI-driven design and applications of quantum dots

    Nano Today · 2026-01-13

    Prince Kumar Soni, Manmohan Lal Satnami, Rekha Nagwanshi, Yogyata Chawre, Ankita Beena Kujur, Akash Sinha, Pinki Miri, Indrapal Karbhal, Kallol Kumar Ghosh

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  33. Graph representation learning for predicting processing conditions in Spinodal decomposition microstructures

    Computational Materials Science · 2026-01-21

    Sulbha Malviya, Emerson Konkol, Mahmood Mamivand

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  34. Counting Spanning Trees with One Perfect Matching in Helicene Polygonal Chains on the Plane and Cylinder

    Graphs and Combinatorics · 2026-02-11

    Sujing Cheng, Jun Ge

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  35. Twin cross contrastive learning with multi-modality fusion for drug–target affinity prediction

    Artificial Intelligence in Medicine · 2026-03-04

    Linna Zhang, Zhaowei Wang, Wuhao Liu, Xiaodong Duan, Qiguo Dai

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  36. Deep Learning for Anticancer Drug Discovery Targeting Non-Apoptotic Regulated Cell Death Mechanisms

    Pharmaceuticals · 2026-05-29

    Mengwan Jiang, Jinlun Mu, Shuoye Yang, Peng Li

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  37. ALiBERT: Transformer-Based NLP and Hybrid Ensemble Learning Framework for Predicting Material Properties

    Arabian Journal for Science and Engineering · 2026-07-21

    M. Subramanian, N. Arunkumar, M. Diviya, T. Balasubramanian

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