Towards large-scale and spatiotemporally resolved diagnosis of electronic density of states by deep learning
| العنوان: | Towards large-scale and spatiotemporally resolved diagnosis of electronic density of states by deep learning |
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| المؤلفون: | Qiyu Zeng, Bo Chen, Xiaoxiang Yu, Shen Zhang, Dongdong Kang, Han Wang, Jiayu Dai |
| المصدر: | Physical Review B. 105 |
| بيانات النشر: | American Physical Society (APS), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Condensed Matter - Materials Science, Materials Science (cond-mat.mtrl-sci), FOS: Physical sciences, Physics - Atomic and Molecular Clusters, Disordered Systems and Neural Networks (cond-mat.dis-nn), Condensed Matter - Disordered Systems and Neural Networks, Computational Physics (physics.comp-ph), Atomic and Molecular Clusters (physics.atm-clus), Physics - Computational Physics |
| الوصف: | Modern laboratory techniques like ultrafast laser excitation and shock compression can bring matter into highly nonequilibrium states with complex structural transformation, metallization and dissociation dynamics. To understand and model the dramatic change of both electronic structures and ion dynamics during such dynamic processes, the traditional method faces difficulties. Here, we demonstrate the ability of deep neural network (DNN) to capture the atomic local-environment dependence of electronic density of states (DOS) for both multicomponent system under exoplanet thermodynamic condition and nonequilibrium system during super-heated melting process. Large scale and time-resolved diagnosis of DOS can be efficiently achieved within the accuracy of ab initio method. Moreover, the atomic contribution to DOS given by DNN model accurately reveals the information of local neighborhood for selected atom, thus can serve as robust order parameters to identify different phases and intermediate local structures, strongly highlights the efficacy of this DNN model in studying dynamic processes. Comment: 7 Figures, accepted by PRB |
| تدمد: | 2469-9969 2469-9950 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d891294696dc5d46b522fbf452af29d8 https://doi.org/10.1103/physrevb.105.174109 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....d891294696dc5d46b522fbf452af29d8 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 24699969 24699950 |
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