期刊
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
卷 61, 期 38, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/anie.202209398
关键词
Ab Initio Molecular Dynamics; Aqueous Electron; Hybrid Functional; Machine Learning
资金
- NCCR MARVEL, a National Centre of Competence in Research - Swiss National Science Foundation [182892]
- Swiss National Supercomputing Centre (CSCS) [s1123]
- Ecole Polytechnique Federale de Lausanne
Using mixed quantum-classical simulations and machine learning techniques, we accurately describe the temperature-dependent properties of the aqueous electron under various thermodynamic conditions. Our work reveals that the red shift of the absorption maximum, in the presence of cavity formation, is due to an increasing gyration radius with temperature, rather than global density variations as previously suggested.
The temperature-dependent properties of the aqueous electron have been extensively studied using mixed quantum-classical simulations in a wide range of thermodynamic conditions based on one-electron pseudopotentials. While the cavity model appears to explain most of the physical properties of the aqueous electron, only a non-cavity model has so far been successful in accounting for the temperature dependence of the absorption spectrum. Here, we present an accurate and efficient description of the aqueous electron under various thermodynamic conditions by combining hybrid functional-based molecular dynamics, machine learning techniques, and multiple time-step methods. Our advanced simulations accurately describe the temperature dependence of the absorption maximum in the presence of cavity formation. Specifically, our work reveals that the red shift of the absorption maximum results from an increasing gyration radius with temperature, rather than from global density variations as previously suggested.
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