Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians
Abstract Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N2AMD (Neural-Network Non-Adiabatic Molecular Dynamics), which employs an E(3)-equivariant deep neural Hami...
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Nature Portfolio
2025-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-57328-1 |
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| author | Changwei Zhang Yang Zhong Zhi-Guo Tao Xinming Qin Honghui Shang Zhenggang Lan Oleg V. Prezhdo Xin-Gao Gong Weibin Chu Hongjun Xiang |
| author_facet | Changwei Zhang Yang Zhong Zhi-Guo Tao Xinming Qin Honghui Shang Zhenggang Lan Oleg V. Prezhdo Xin-Gao Gong Weibin Chu Hongjun Xiang |
| author_sort | Changwei Zhang |
| collection | DOAJ |
| description | Abstract Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N2AMD (Neural-Network Non-Adiabatic Molecular Dynamics), which employs an E(3)-equivariant deep neural Hamiltonian to boost the accuracy and efficiency of NAMD simulations. Distinct from conventional machine learning methods that predict key quantities in NAMD, N2AMD computes these quantities directly with a deep neural Hamiltonian, ensuring excellent accuracy, efficiency, and consistency. N2AMD not only achieves impressive efficiency in performing NAMD simulations at the hybrid functional level within the framework of the classical path approximation (CPA), but also demonstrates great potential in predicting non-adiabatic coupling vectors and suggests a method to go beyond CPA. Furthermore, N2AMD demonstrates excellent generalizability and enables seamless integration with advanced NAMD techniques and infrastructures. Taking several extensively investigated semiconductors as the prototypical system, we successfully simulate carrier recombination in both pristine and defective systems at large scales where conventional NAMD often significantly underestimates or even qualitatively incorrectly predicts lifetimes. This framework offers a reliable and efficient approach for conducting accurate NAMD simulations across various condensed materials. |
| format | Article |
| id | doaj-art-9a248ada2b6e4bf7bfd441f722c66b69 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9a248ada2b6e4bf7bfd441f722c66b692025-08-20T02:59:35ZengNature PortfolioNature Communications2041-17232025-02-0116111310.1038/s41467-025-57328-1Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltoniansChangwei Zhang0Yang Zhong1Zhi-Guo Tao2Xinming Qin3Honghui Shang4Zhenggang Lan5Oleg V. Prezhdo6Xin-Gao Gong7Weibin Chu8Hongjun Xiang9Key Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, and Department of Physics, Fudan UniversityKey Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, and Department of Physics, Fudan UniversityKey Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, and Department of Physics, Fudan UniversityKey Laboratory of Precision and Intelligent Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of ChinaKey Laboratory of Precision and Intelligent Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of ChinaSCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal UniversityDepartment of Chemistry and Department of Physics & Astronomy, University of Southern CaliforniaKey Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, and Department of Physics, Fudan UniversityKey Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, and Department of Physics, Fudan UniversityKey Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, and Department of Physics, Fudan UniversityAbstract Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N2AMD (Neural-Network Non-Adiabatic Molecular Dynamics), which employs an E(3)-equivariant deep neural Hamiltonian to boost the accuracy and efficiency of NAMD simulations. Distinct from conventional machine learning methods that predict key quantities in NAMD, N2AMD computes these quantities directly with a deep neural Hamiltonian, ensuring excellent accuracy, efficiency, and consistency. N2AMD not only achieves impressive efficiency in performing NAMD simulations at the hybrid functional level within the framework of the classical path approximation (CPA), but also demonstrates great potential in predicting non-adiabatic coupling vectors and suggests a method to go beyond CPA. Furthermore, N2AMD demonstrates excellent generalizability and enables seamless integration with advanced NAMD techniques and infrastructures. Taking several extensively investigated semiconductors as the prototypical system, we successfully simulate carrier recombination in both pristine and defective systems at large scales where conventional NAMD often significantly underestimates or even qualitatively incorrectly predicts lifetimes. This framework offers a reliable and efficient approach for conducting accurate NAMD simulations across various condensed materials.https://doi.org/10.1038/s41467-025-57328-1 |
| spellingShingle | Changwei Zhang Yang Zhong Zhi-Guo Tao Xinming Qin Honghui Shang Zhenggang Lan Oleg V. Prezhdo Xin-Gao Gong Weibin Chu Hongjun Xiang Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians Nature Communications |
| title | Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians |
| title_full | Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians |
| title_fullStr | Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians |
| title_full_unstemmed | Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians |
| title_short | Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians |
| title_sort | advancing nonadiabatic molecular dynamics simulations in solids with e 3 equivariant deep neural hamiltonians |
| url | https://doi.org/10.1038/s41467-025-57328-1 |
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