| The 2021 Room-Temperature Superconductivity Roadmap L Boeri, RG Hennig, PJ Hirschfeld, G Profeta, A Sanna, E Zurek, ... Journal of Physics: Condensed Matter, 2021 | 211* | 2021 |
| Fast nastic motion of plants and bioinspired structures Q Guo, E Dai, X Han, S Xie, E Chao, Z Chen Journal of the Royal Society Interface 12 (110), 20150598, 2015 | 136 | 2015 |
| Machine learning of superconducting critical temperature from Eliashberg theory SR Xie, Y Quan, AC Hire, B Deng, JM DeStefano, I Salinas, US Shah, ... npj Computational Materials 8 (1), 14, 2022 | 61 | 2022 |
| Functional form of the superconducting critical temperature from machine learning SR Xie, GR Stewart, JJ Hamlin, PJ Hirschfeld, RG Hennig Physical Review B 100 (17), 174513, 2019 | 59 | 2019 |
| Ultra-fast interpretable machine-learning potentials SR Xie, M Rupp, RG Hennig npj Computational Materials 9 (1), 162, 2023 | 42 | 2023 |
| Creating superconductivity in WB2 through pressure-induced metastable planar defects J Lim, AC Hire, Y Quan, JS Kim, SR Xie, S Sinha, RS Kumar, D Popov, ... Nature communications 13 (1), 7901, 2022 | 34 | 2022 |
| Machine learning of octahedral tilting in oxide perovskites by symbolic classification with compressed sensing SR Xie, P Kotlarz, RG Hennig, JC Nino Computational Materials Science 180, 109690, 2020 | 27 | 2020 |
| Augmenting machine learning of energy landscapes with local structural information SJ Honrao, SR Xie, RG Hennig Journal of Applied Physics 128 (8), 2020 | 19 | 2020 |
| Candidate replacements for lead in CH3NH3PbI3 from first principles calculations JJ Gabriel, S Xie, K Choudhary, M Sexton, SR Phillpot, J Xue, RG Hennig Computational Materials Science 155, 69-73, 2018 | 12 | 2018 |
| High-pressure study of the low- rich superconductor J Lim, AC Hire, Y Quan, J Kim, L Fanfarillo, SR Xie, RS Kumar, C Park, ... Physical Review B 104 (6), 064505, 2021 | 10 | 2021 |
| Computational Design of Low Melting Eutectics of Molten Salts: A Combined Machine Learning and Thermodynamic Modeling Approach A Ravichandran, S Honrao, S Xie, E Fonseca, JW Lawson The Journal of Physical Chemistry Letters 15 (1), 121-126, 2023 | 3 | 2023 |
| Stability and magnetic behavior of exfoliable nanowire one-dimensional materials JT Paul, J Lu, S Shah, SR Xie, RG Hennig Physical Review Materials 7 (7), 076002, 2023 | 3 | 2023 |
| High-Throughput Screening of Li Solid-State Electrolytes with Bond Valence Methods and Machine Learning SR Xie, SJ Honrao, JW Lawson Chemistry of Materials 36 (19), 9320-9329, 2024 | 2 | 2024 |
| Ultra-Fast Force Fields (UF3) framework for machine-learning interatomic potentials S Xie, R Schmid, M Rupp, R Hennig APS March Meeting Abstracts 2022, G13. 004, 2022 | 2 | 2022 |
| Remarkable low-energy properties of the pseudogapped semimetal L Fanfarillo, JJ Hamlin, RG Hennig, AC Hire, PJ Hirschfeld, J Kim, J Lim, ... Physical Review B 102 (15), 155206, 2020 | 2 | 2020 |
| 18. Towards high-throughput superconductor discovery via machine learning SR Xie, Y Quan, AC Hire, L Fanfarillo, GR Stewart, JJ Hamlin, RG Hennig, ... The 2021 Room-Temperature Superconductivity Roadmap, 2021 | 1 | 2021 |
| Inverse Design of Materials with Lab Automation and AI-driven Experimentation J Stuckner, D Santiago-deJesus, P Toma, R Obregon, J Goodin, ... International Materials Application & Technologies Conference and Exposition …, 2024 | | 2024 |
| Ultra-fast machine-learning potentials to simulate spin-lattice dynamics of magnetism in iron M Li, A Hire, S Xie, R Hennig Bulletin of the American Physical Society, 2024 | | 2024 |
| Screening of Li-Based Solid Electrolytes Using Bond-Valence Methods and Graph Neural Networks SR Xie, SJ Honrao, JW Lawson 153rd Annual Meeting and Exhibition of the Minerals, Metals & Materials …, 2024 | | 2024 |
| High-Throughput Screening of Li Solid-State Electrolytes With Bond Valence Methods and Graph Neural Networks SR Xie, SJ Honrao, JW Lawson TMS 153rd Annual Meeting & Exhibition, 2024 | | 2024 |
Richard G. HennigUniversity of Florida在 ufl.edu 的电子邮件经过验证