Electronically Nonadiabatic Dynamics of O2 + O Collisions on Sixteen New Machine-Learned 3 A ″ Global Potential Energy Surfaces

IF 5.8 1区 化学 Q2 CHEMISTRY, PHYSICAL
Qinghui Meng,Yinan Shu,Zoltan Varga,Donald G. Truhlar
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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.
十六种新机器学习的3a″全球势能面上O2 + O碰撞的电子非绝热动力学
O3的3A″势表面流形上的高能O2 + O碰撞对多态非绝热动力学提出了挑战,因为共价价态和离子构型在动态可达区域内发生了强烈的混合。我们开发了一个全局机器学习兼容势能矩阵(CPEM),用于具有3A″对称的O2(X) + O(3P)动力学的16态非绝热描述,该矩阵基于XMS-CASPT2数据进行训练,并在O2+ + O -离子特征重要的地方进行物理动机处理。对角化得到的绝热曲面具有解析梯度和正确的圆锥交拓扑结构,在三原子极限和O2 + O极限下均具有较好的精度。这建立了一种学习具有混合共价离子特性的耦合势能表面密集流形的一般策略。利用渐近扩展的曲率驱动相干开关混合衰减(AE-κCSDM)动力学方法,我们计算了在大范围的平动能和O2旋转振动态下的电子非弹性和反应截面。模拟揭示了交换反应性、适度的电子非绝热分支以及电子态分辨激发的系统趋势,这些趋势是振动量子数、旋转激发和动态能量的函数。
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来源期刊
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation 化学-物理:原子、分子和化学物理
CiteScore
9.90
自引率
16.40%
发文量
568
审稿时长
1 months
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Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere.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.
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