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RESEARCH PAPER · Advanced Science · 2022

Formula Graph Self‐Attention Network for Representation‐Domain Independent Materials Discovery

Achintha Avin Ihalage, Yang Hao

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Formula Graph Self‐Attention Network for Representation‐Domain Independent Materials DiscoveryOriginalCrystal twins: self-supervised learning for crystalline material property prediction72Extrapolation performance improvement by quantum chemical calculations for machine-learning-based predictions of flow-synthesized binary copolymers9A critical examination of robustness and generalizability of machine learning prediction of materials properties146Identification of Mild cognitive impairment based on quadruple GCN model constructed with multiple features from higher-order brain connectivity12Gradient boosted and statistical feature selection workflow for materials property predictions27Pretraining Strategies for Structure Agnostic Material Property Prediction17Cationic perturbation strategy to solve the information drought in material explainable machine learning9Single-atom catalysts property prediction via Supervised and Self-Supervised pre-training models15Bayesian optimization of radical polymerization reactions in a flow synthesis system2Bayesian optimization of radical polymerization reactions in a flow synthesis system1Rational Design of Deep Learning Networks Based on a Fusion Strategy for Improved Material Property Predictions12Ionic species representations for materials informatics6Bayesian optimization of radical polymerization reactions in a flow synthesis system7DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules41Digital features of chemical elements extracted from local geometries in crystal structures2Machine learning-driven breakthroughs in water electrolysis and supercapacitors17Molecular representation learning: cross-domain foundations and future Frontiers27QiGSAN: A Novel Probability-Informed Approach for Small Object Segmentation in the Case of Limited Image Datasets0SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction6Two-stage transfer learning for deep learning-based prediction of lattice thermal conductivity0CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning1CoFiNet: a coarse-to-fine attention network for structure-agnostic materials property prediction0Spatio-temporal prediction of dendrite growth based on long-short period graph neural network–wavelet signal transformer0Multi-model structure-agnostic framework for enhanced materials discovery in engineering informatics1ARPES-Inspired Reciprocal-Space Crystal Property Predictor0

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  1. Crystal twins: self-supervised learning for crystalline material property prediction

    npj Computational Materials · 2022-11-10

    Rishikesh Magar, Yuyang Wang, Amir Barati Farimani

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  2. Extrapolation performance improvement by quantum chemical calculations for machine-learning-based predictions of flow-synthesized binary copolymers

    Digital Discovery · 2023-01-01

    Shogo Takasuka, Shunto Oikawa, Takayoshi Yoshimura, Shô Itô, Yosuke Harashima, Tomoaki Takayama, Shigehito Asano, Akira Kurosawa, Tetsunori Sugawara, Miho Hatanaka, Tomoyuki Miyao, Takamitsu Matsubara, Yu‐ya Ohnishi, Hiroharu Ajiro, Mikiya Fujii

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  3. A critical examination of robustness and generalizability of machine learning prediction of materials properties

    npj Computational Materials · 2023-04-07

    Kangming Li, Brian DeCost, Kamal Kumar Choudhary, Michael Greenwood, Jason R. Hattrick-Simpers

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  4. Identification of Mild cognitive impairment based on quadruple GCN model constructed with multiple features from higher-order brain connectivity

    Expert Systems with Applications · 2023-06-01

    Yuan Li, Ying Zou, Hanning Guo, Yongqing Yang, Na Li, Linhao Li, Feng Zhao

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  5. Gradient boosted and statistical feature selection workflow for materials property predictions

    The Journal of Chemical Physics · 2023-11-16

    Son Gyo Jung, Guwon Jung, Jacqueline M. Cole

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  6. Pretraining Strategies for Structure Agnostic Material Property Prediction

    Journal of Chemical Information and Modeling · 2024-02-01

    Hongshuo Huang, Rishikesh Magar, Amir Barati Farimani

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  7. Cationic perturbation strategy to solve the information drought in material explainable machine learning

    Physical review. B./Physical review. B · 2024-02-13

    Zhengxin Chen, Hange Wang, Bowen Liu, Hairui Zhou, Yingjie Cao, Yanan Wang, Peng Lin, Xiaolin Liu, Jia Horng Lin, Xianfeng Chen, Jiang Wu

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  8. Single-atom catalysts property prediction via Supervised and Self-Supervised pre-training models

    Chemical Engineering Journal · 2024-03-24

    Lanjing Wang, Honghao Chen, Longqi Yang, Jiali Li, Yong Li, Xiaonan Wang

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  9. Bayesian optimization of radical polymerization reactions in a flow synthesis system

    ChemRxiv · 2024-03-29

    Shogo Takasuka, Shô Itô, Shunto Oikawa, Yosuke Harashima, Tomoaki Takayama, Aniruddha Nag, Araki Wakiuchi, Tsuyoshi Ando, Tetsunori Sugawara, Miho Hatanaka, Tomoyuki Miyao, Takamitsu Matsubara, Yu‐ya Ohnishi, Hiroharu Ajiro, Mikiya Fujii

    Read original paper ↗ ·

  10. Bayesian optimization of radical polymerization reactions in a flow synthesis system

    ChemRxiv · 2024-04-05

    Shogo Takasuka, Shô Itô, Shunto Oikawa, Yosuke Harashima, Tomoaki Takayama, Aniruddha Nag, Araki Wakiuchi, Tsuyoshi Ando, Tetsunori Sugawara, Miho Hatanaka, Tomoyuki Miyao, Takamitsu Matsubara, Yu‐ya Ohnishi, Hiroharu Ajiro, Mikiya Fujii

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  11. Rational Design of Deep Learning Networks Based on a Fusion Strategy for Improved Material Property Predictions

    Journal of Chemical Theory and Computation · 2024-07-18

    Hongwei Du, Jian Hui, Lanting T. Zhang, Hong Wang

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  12. Ionic species representations for materials informatics

    APL Machine Learning · 2024-09-01

    Anthony Onwuli, Keith T. Butler, Aron Walsh

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  13. Bayesian optimization of radical polymerization reactions in a flow synthesis system

    Science and Technology of Advanced Materials Methods · 2024-11-13

    Shogo Takasuka, Shô Itô, Shunto Oikawa, Yosuke Harashima, Tomoaki Takayama, Aniruddha Nag, Araki Wakiuchi, Tsuyoshi Ando, Tetsunori Sugawara, Miho Hatanaka, Tomoyuki Miyao, Takamitsu Matsubara, Yu‐ya Ohnishi, Hiroharu Ajiro, Mikiya Fujii

    Read original paper ↗ ·

  14. DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules

    npj Computational Materials · 2024-12-19

    Hongwei Du, Jiamin Wang, Jian Hui, Lanting T. Zhang, Hong Wang

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  15. Digital features of chemical elements extracted from local geometries in crystal structures

    Digital Discovery · 2025-01-01

    Andrij Vasylenko, Dmytro Antypov, Sven Schewe, Luke M. Daniels, John B. Claridge, Matthew Stephen Dyer, Matthew J. Rosseinsky

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  16. Machine learning-driven breakthroughs in water electrolysis and supercapacitors

    Materials Chemistry Frontiers · 2025-01-01

    Diab Khalafallah, Fuming Lai, Hao Huang, Jue Wang, Xiaoqing Wang, Shengfu Tong, Qinfang Zhang

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  17. Molecular representation learning: cross-domain foundations and future Frontiers

    Digital Discovery · 2025-01-01

    Rahul Sheshanarayana, Fengqi You

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  18. QiGSAN: A Novel Probability-Informed Approach for Small Object Segmentation in the Case of Limited Image Datasets

    Big Data and Cognitive Computing · 2025-09-18

    Andrey Konstantinovich Gorshenin, Anna Dostovalova

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  19. SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction

    npj Computational Materials · 2025-12-18

    Junchi Liu, Yibang Tang, Sergei Tretiak, Wenhui Duan, Liujiang Zhou

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  20. Two-stage transfer learning for deep learning-based prediction of lattice thermal conductivity

    Physical Chemistry Chemical Physics · 2026-01-01

    L. Klochko, Mathieu d’Aquin

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  21. CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning

    Nature Communications · 2026-03-17

    Changwen Xu, Shang Zhu, Venkatasubramanian Viswanathan

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  22. CoFiNet: a coarse-to-fine attention network for structure-agnostic materials property prediction

    Molecular Physics · 2026-05-27

    Bangliang Jia, Huaijuan Zang, Hailong Zhang, Jiajia Xu, Shu Zhan

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  23. Spatio-temporal prediction of dendrite growth based on long-short period graph neural network–wavelet signal transformer

    Journal of Materials Research and Technology · 2026-07-01

    Cijun Shuai, Hui Chen, Zhuangji Yu, Mingli Yang, Xiong Shuai, Jing Zhang, Fangwei Qi

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  24. Multi-model structure-agnostic framework for enhanced materials discovery in engineering informatics

    Advanced Engineering Informatics · 2026-07-08

    Altaf Hussain, Muhammad Munsif, Zulfiqar Ahmad Khan, Kaleem Ullah, Kee‐Sun Sohn, Sung Wook Baik

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  25. ARPES-Inspired Reciprocal-Space Crystal Property Predictor

    PRX Intelligence · 2026-07-28

    Anonymous, Xin-Yi Liu, Chuan-Nan Li, Jinshan Li, Xie Zhang

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