Search for: All records

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. 

Mixed-reference spin-flip time-dependent density functional theory (MRSF-TDDFT) improves upon the original spin-flip TDDFT (SF-TDDFT) approach by expanding the configurational space of the response states via a mixed-reference reduced density matrix, specifically, an equal mixture of the density matrices from the MS = +1 and MS = −1 components of the lowest triplet state. MRSF-TDDFT improves the description of the spin-flip target states and the respective energy differences and eliminates the problem of spin-contamination that often spoils the description of higher-lying states in SF-TDDFT. This article presents the theoretical framework and details of a new implementation of MRSF-TDDFT in the Q-Chem software package. The implementation includes calculations of energies and properties, including transition dipole moments, oscillator strengths, spin–orbit couplings, and density-matrix based analyses of the MRSF states and transitions. The capabilities of the theory are illustrated by applications to diradicals, butadiene, and a reduced retinal model, the penta-2,4-dieniminium cation, used to evaluate the performance of electronic structure methods in describing conical intersections. 

We propose the generalized initial velocity sampling algorithm at a transition state, in which the total initial kinetic energy and extra positive initial velocity along the reaction coordinate have been introduced to improve the accuracy and efficiency of nonadiabatic dynamics simulation. This sampling algorithm is very useful for chemical reactions with multiple transition states, e.g., two transition states in the thermolysis of four-membered heterocyclic peroxides. The dependence of the chemiexcitation yields, dissociation times, and other quantities on the total initial kinetic energy and extra positive initial velocity has been investigated. By taking different CASPT2 corrections and additional positive initial velocities into account, we found that the resulting triplet quantum yield matches the experimental result perfectly. In most ensembles, the secondary primary intersystem crossing, i.e., the first singlet excited state to the first triplet state, is a major direct production channel for the first triplet state product trajectories.