Zobrazeno 1 - 10
of 397
pro vyhledávání: '"Lan, Zhenggang"'
Autor:
Zhang, Changwei, Zhong, Yang, Tao, Zhi-Guo, Qing, Xinming, Shang, Honghui, Lan, Zhenggang, Prezhdo, Oleg V., Gong, Xin-Gao, Chu, Weibin, Xiang, Hongjun
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, N$^2$AMD which employs an E(3)-equivariant deep neural Hamiltonian
Externí odkaz:
http://arxiv.org/abs/2408.06654
Due to rapid advancements in deep learning techniques, the demand for large-volume high-quality databases grows significantly in chemical research. We developed a quantum-chemistry database that includes 443,106 small organic molecules with sizes up
Externí odkaz:
http://arxiv.org/abs/2406.02341
The comprehension of nonadiabatic dynamics in polyatomic systems relies heavily on the simultaneous advancements in theoretical and experimental domains. The gas-phase electron diffraction (GUED) technique has attracted widespread attention as a prom
Externí odkaz:
http://arxiv.org/abs/2402.08900
Singlet fission (SF) is a very significant photophysical phenomenon and possesses potential applications. In this work, we try to give the rather detailed theoretical investigation of the SF process in the stacked polyacene dimer by combining the hig
Externí odkaz:
http://arxiv.org/abs/2308.16392
We developed an automated approach to construct the complex reaction network and explore the reaction mechanism for several reactant molecules. The nanoreactor type molecular dynamics was employed to generate possible chemical reactions, in which the
Externí odkaz:
http://arxiv.org/abs/2306.14130
The supervised machine learning (ML) approach is applied to realize the trajectory-based nonadiabatic dynamics within the framework of the symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian (MM-SQC). After the c
Externí odkaz:
http://arxiv.org/abs/2207.05556
The machine learning approaches are applied in the dynamical simulation of open quantum systems. The long short-term memory recurrent neural network (LSTM-RNN) models are used to simulate the long-time quantum dynamics, which are built based on the k
Externí odkaz:
http://arxiv.org/abs/2205.03600
Publikováno v:
Journal of Chemical Physics; 9/28/2024, Vol. 161 Issue 12, p1-14, 14p
The ultrafast nonadiabatic internal conversion in azomethane is explored by the on-the-fly trajectory surface-hopping simulations of photoinduced dynamics and femtosecond transient absorption (TA) pump-probe (PP) spectra at three electronic-structure
Externí odkaz:
http://arxiv.org/abs/2110.14901
We develop a broadly-applicable computational method for the automatic exploration of the bimolecular multi-reaction mechanism. The current methodology mainly involves the high-energy Born-Oppenheimer molecular dynamics (BOMD) simulation and the succ
Externí odkaz:
http://arxiv.org/abs/2108.03104