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RESEARCH ARTICLE · DSpace@MIT (Massachusetts Institute of Technology) · 2021

Direct Prediction of Phonon Density of States With Euclidean Neural Networks

Zhantao Chen, Nina Andrejevic, Tess Smidt, Zhiwei Ding, Qian Xu, Yen‐Ting Chi, Nguyen, Quynh T, Ahmet Alatas, Jing Kong, Mingda Li

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Direct Prediction of Phonon Density of States With Euclidean Neural NetworksOriginalThe Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety303A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches242Machine learning on neutron and x-ray scattering and spectroscopies116Artificial intelligence for search and discovery of quantum materials68Can a shock-induced phonon up-pumping model relate to impact sensitivity of molecular crystals, polymorphs and cocrystals?19Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings91Recent advances and applications of deep learning methods in materials science1161Formula Graph Self‐Attention Network for Representation‐Domain Independent Materials Discovery26Mechanism of local lattice distortion effects on vacancy migration barriers in fcc alloys10Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science20Machine learning magnetism classifiers from atomic coordinates28A data-science approach to predict the heat capacity of nanoporous materials117The iron group transition-metal (Fe, Ru, Os) coordination of Se-doped graphitic carbon (Se@g-C 3 N 4 ) nanostructures for the smart therapeutic delivery of zidovudine (ZVD) as an antiretroviral drug: a theoretical calculation perspective7Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis13Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity96Inspired: Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design0Machine learning in neutron scattering data analysis4Kinetic Monte Carlo simulations of solute clustering during quenching and aging of Al–Mg–Zn alloys24INSPIRED: Inelastic neutron scattering prediction for instantaneous results and experimental design5Virtual node graph neural network for full phonon prediction25Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals11Rapid prediction of phonon density of states by crystal attention graph neural network and high-throughput screening of candidate substrates for wide bandgap electronic cooling11Accelerating high-throughput phonon calculations via machine learning universal potentials19Heat capacity estimation of complex materials for energy technologies11Efficient modeling of dynamic properties in K3C60 using machine learning force fields0

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  1. The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety

    Joule · 2020

    Donal P. Finegan, Juner Zhu, Xuning Feng, Matthew A. Keyser, Marcus Ulmefors, Wei Li, Martin Z. Bazant, Samuel J. Cooper

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  2. A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches

    Computer Methods in Applied Mechanics and Engineering · 2021

    Wei Li, Martin Z. Bazant, Juner Zhu

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  3. Machine learning on neutron and x-ray scattering and spectroscopies

    Chemical Physics Reviews · 2021

    Zhantao Chen, Nina Andrejevic, Nathan C. Drucker, Thanh Nguyen, Rui Patrick Xian, Tess Smidt, Yao Wang, Ralph Ernstorfer, D. M. Tennant, Maria K. Y. Chan, Mingda Li

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  4. Artificial intelligence for search and discovery of quantum materials

    Communications Materials · 2021

    Valentin Stanev, Kamal Kumar Choudhary, Aaron Gilad Kusne, Johnpierre Paglione, Ichiro Takeuchi

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  5. Can a shock-induced phonon up-pumping model relate to impact sensitivity of molecular crystals, polymorphs and cocrystals?

    RSC Advances · 2022

    Xavier Bidault, Santanu Chaudhuri

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

    Nature Communications · 2022

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

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  7. Recent advances and applications of deep learning methods in materials science

    npj Computational Materials · 2022

    Kamal Kumar Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol Woo Park, Alok Choudhary, Ankit Agrawal, Simon J. L. Billinge, Elizabeth A. Holm, Shyue Ping Ong, Chris M. Wolverton

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

    Advanced Science · 2022

    Achintha Avin Ihalage, Yang Hao

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  9. Mechanism of local lattice distortion effects on vacancy migration barriers in fcc alloys

    Physical Review Materials · 2022

    Zhucong Xi, Mingfei Zhang, Louis G. Hector, Amit Misra, Qi Liang

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  10. Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science

    Nature Communications · 2022

    Charlotte Loh, Thomas Christensen, Rumen Dangovski, Samuel Kim, Marin Soljacic A

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  11. Machine learning magnetism classifiers from atomic coordinates

    iScience · 2022

    Helena Merker, Harry Heiberger, Linh Thi Hoai Nguyen, Tongtong Liu, Zhantao Chen, Nina Andrejevic, Nathan C. Drucker, Ryotaro Okabe, Song Eun Kim, Yao Wang, Tess Smidt, Mingda Li

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  12. A data-science approach to predict the heat capacity of nanoporous materials

    Nature Materials · 2022

    Seyed Mohamad Moosavi, Balázs Álmos Novotny, Daniele Ongari, Elias Moubarak, Mehrdad Asgari, Özge Kadioglu, Charithea Charalambous, Andres Ortega‐Guerrero, Amir Hajiahmadi Farmahini, Lev Sarkisov, Susana García, Frank Noé, Berend Smit

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  13. The iron group transition-metal (Fe, Ru, Os) coordination of Se-doped graphitic carbon (Se@g-C 3 N 4 ) nanostructures for the smart therapeutic delivery of zidovudine (ZVD) as an antiretroviral drug: a theoretical calculation perspective

    RSC Advances · 2023

    F Nelson, Hitler Louis, Innocent Benjamin, Rawlings A. Timothy

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  14. Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis

    Chemistry of Materials · 2023

    Daniel Enrique Vizoso, Ghatu Subhash, Krishna Rajan, Rémi Dingreville

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  15. Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity

    Science Advances · 2023

    Sheng Rong Gong, Keqiang Yan, Tian Xie, Yang Shao‐Horn, Rafael Gómez‐Bombarelli, Shuiwang Ji, Jeffrey C. Grossman

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  16. Inspired: Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design

    SSRN Electronic Journal · 2024

    Bowen Han, Andrei T. Savici, Mingda Li, Yongqiang Q. Cheng

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  17. Machine learning in neutron scattering data analysis

    Journal of Radiation Research and Applied Sciences · 2024

    Hao Wang, Rong Du, Zhiyong Liu, Junrong Zhang

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  18. Kinetic Monte Carlo simulations of solute clustering during quenching and aging of Al–Mg–Zn alloys

    Acta Materialia · 2024

    Zhucong Xi, Louis G. Hector, Amit Misra, Qi Liang

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  19. INSPIRED: Inelastic neutron scattering prediction for instantaneous results and experimental design

    Computer Physics Communications · 2024

    Bowen Han, Andrei T. Savici, Mingda Li, Yongqiang Q. Cheng

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  20. Virtual node graph neural network for full phonon prediction

    Nature Computational Science · 2024

    Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nina Andrejevic, Xiang Fu, Tommi Jaakkola, Qichen Song, Thanh Nguyen, Nathan C. Drucker, Sai Mu, Yao Wang, Bolin Liao, Yongqiang Q. Cheng, Mingda Li

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  21. Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals

    Journal of Chemical Information and Modeling · 2024

    Shokirbek A. Shermukhamedov, Dilorom Mamurjonova, Thana Maihom, Michael M. Probst

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  22. Rapid prediction of phonon density of states by crystal attention graph neural network and high-throughput screening of candidate substrates for wide bandgap electronic cooling

    Materials Today Physics · 2024

    Mohammed Al‐Fahdi, Changpeng Lin, Chen Shen, Hongbin Zhang, Ming Hu

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  23. Accelerating high-throughput phonon calculations via machine learning universal potentials

    Materials Today Physics · 2025

    Huiju Lee, Vinay I. Hegde, Chris M. Wolverton, Yi Xia

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  24. Heat capacity estimation of complex materials for energy technologies

    Joule · 2025

    Elana J. Cope, Joana Bustamante, Zöe M. Johnson, Alicia Lancaster, Ramya L. Gurunathan, Janine George, Matthias T. Agne

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  25. Efficient modeling of dynamic properties in K3C60 using machine learning force fields

    Chemical Physics Impact · 2025

    Ran Mo, Zhishuo Huang, Liviu Ungur

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