Qinghui Meng,Yinan Shu,Zoltan Varga,Donald G. Truhlar
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引用次数: 0
Abstract
Abstract High-energy O2 + O collisions on the 3A″ potential surface manifold of O3 provide a challenge for multistate nonadiabatic dynamics because covalent valence states and ionic configurations become strongly mixed in dynamically accessible regions. We developed a global machine-learned compatible potential-energy matrix (CPEM) for a 16-state nonadiabatic description of O2(X) + O(3P) dynamics with 3A″ symmetry, trained on XMS-CASPT2 data and augmented with physically motivated treatments of O2+ + O– ionic character where they are important. The adiabatic surfaces obtained by diagonalization have the analytic gradients and the correct conical-intersection topology, and they achieve good accuracy in both triatomic and O2 + O asymptotic limits. This establishes a general strategy for learning dense manifolds of coupled potential-energy surfaces with mixed covalent–ionic character. Using these surfaces with the asymptotically extended curvature-driven coherent switching with decay of mixing (AE-κCSDM) dynamics method, we computed electronically inelastic and reactive cross sections over a wide range of translational energies and O2 rovibrational states. The simulations reveal exchange reactivity, modest electronically nonadiabatic branching, and systematic trends in electronic-state-resolved excitation as functions of vibrational quantum number, rotational excitation, and dynamical energy.
期刊介绍:
The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.