Akher Farideh Badichi, Shu Yinan, Varga Zoltan, Truhlar Donald G
Department of Chemistry, Chemical Theory Center and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States.
J Chem Theory Comput. 2023 Jul 25;19(14):4389-4401. doi: 10.1021/acs.jctc.3c00517. Epub 2023 Jul 13.
Dynamics simulations of high-energy O-O collisions play an important role in simulating thermal energy content and heat flux in flows around hypersonic vehicles. To carry out such dynamics simulations efficiently requires accurate global potential energy surfaces and (in most algorithms) state couplings for many energetically accessible electronic states. The ability to treat collisions involving many coupled electronic states has been a challenge for decades. Very recently, a new diabatization method, the parametrically managed diabatization by deep neural network (PM-DDNN), has been developed. The PM-DDNN method uses a deep neural network architecture with an activation function parametrically dependent on input data to discover and fit the diabatic potential energy matrix (DPEM) as a function of geometry, and the adiabatic potential energy surfaces are obtained by diagonalization of a small matrix with analytic matrix elements. Here, we applied the PM-DDNN method to the six lowest-energy potential energy surfaces in the ' manifold of O to perform simultaneous diabatization and fitting; the data are obtained by extended multistate complete-active-space second-order perturbation theory. We then used the adiabatic surfaces for dynamics calculations with three methods: coherent switching with decay of mixing (CSDM), curvature-driven CSDM (κCSDM), and electronically curvature-driven CSDM (eκCSDM). The κCSDM calculations require only adiabatic potential energies and gradients. The three dynamical methods are in good agreement. We then calculated electronically nonadiabatic, electronically inelastic, and dissociative cross sections for seven initial collision energies, five initial vibrational levels, and four initial rotational levels. Trends in the electronically inelastic cross sections as functions of the initial collision energy and vibrational level were rationalized in terms of the coordinate ranges where the gaps between the second and third potential energy surfaces are small.
高能氧-氧碰撞的动力学模拟在模拟高超声速飞行器周围流场的热能含量和热通量方面起着重要作用。要高效地进行此类动力学模拟,需要针对许多能量上可及的电子态有精确的全局势能面和(在大多数算法中)态耦合。几十年来,处理涉及许多耦合电子态的碰撞一直是一项挑战。最近,一种新的 diabatic 化方法——深度神经网络参数化管理 diabatic 化(PM-DDNN)被开发出来。PM-DDNN 方法使用一种深度神经网络架构,其激活函数参数化地依赖于输入数据,以发现并拟合作为几何函数的 diabatic 势能矩阵(DPEM),并且通过对具有解析矩阵元的小矩阵进行对角化来获得绝热势能面。在此,我们将 PM-DDNN 方法应用于 O₂ 流形中的六个最低能量势能面,以进行同时的 diabatic 化和拟合;数据通过扩展多态完全活性空间二阶微扰理论获得。然后,我们使用绝热面通过三种方法进行动力学计算:混合衰减的相干切换(CSDM)、曲率驱动的 CSDM(κCSDM)和电子曲率驱动的 CSDM(eκCSDM)。κCSDM 计算仅需要绝热势能和梯度。这三种动力学方法吻合良好。然后,我们计算了七种初始碰撞能量、五种初始振动能级和四种初始转动能级下的电子非绝热、电子非弹性和解离截面。电子非弹性截面随初始碰撞能量和振动能级的变化趋势根据第二和第三势能面之间间隙较小的坐标范围进行了合理解释。