Professional Education


  • Bachelor of Science, Peking University (2007)
  • Doctor of Philosophy, Johns Hopkins University (2014)

Stanford Advisors


All Publications


  • Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices JOURNAL OF CHEMICAL PHYSICS Xie, C., Zhu, X., Yarkony, D. R., Guo, H. 2018; 149 (14): 144107

    Abstract

    A machine learning method is proposed for representing the elements of diabatic potential energy matrices (PEMs) with high fidelity. This is an extension of the so-called permutation invariant polynomial-neural network (PIP-NN) method for representing adiabatic potential energy surfaces. While for one-dimensional irreducible representations the diagonal elements of a diabatic PEM are invariant under exchange of identical nuclei in a molecular system, the off-diagonal elements require special symmetry consideration, particularly in the presence of a conical intersection. A multiplicative factor is introduced to take into consideration the particular symmetry properties while maintaining the PIP-NN framework. We demonstrate here that the extended PIP-NN approach is accurate in representing diabatic PEMs, as evidenced by small fitting errors and by the reproduction of absorption spectra and product branching ratios in both H2O( X ̃ / B ̃ ) and NH3( X ̃ / Ã ) non-adiabatic photodissociation.

    View details for DOI 10.1063/1.5054310

    View details for Web of Science ID 000447149500011

    View details for PubMedID 30316273

  • Imaging CF3I conical intersection and photodissociation dynamics with ultrafast electron diffraction Science Yang, J., Zhu, X., Wolf, T. J., Li, Z., Nunes, J. F., Coffee, R., Cryan, J. P., Gühr, M., Hegazy, K., Heinz, T. F., Jobe, K., Li, R., Shen, X., Veccione, T., Weathersby, S., Wilkin, K. J., Yoneda, C., Zheng, Q., Martinez, T. J., Centurion, M., Wang, X. 2018; 361 (6397): 64-67

    View details for DOI 10.1126/science.aat0049

  • Understanding the mechanochemistry of molecular ladders Chen, Z., Chen, L., Mercer, J., Zhu, X., Martinez, T., Burns, N., Xia, Y. AMER CHEMICAL SOC. 2017
  • On the incorporation of the geometric phase in general single potential energy surface dynamics: A removable approximation to ab initio data JOURNAL OF CHEMICAL PHYSICS Malbon, C. L., Zhu, X., Guo, H., Yarkony, D. R. 2016; 145 (23)

    Abstract

    For two electronic states coupled by conical intersections, the line integral of the derivative coupling can be used to construct a complex-valued multiplicative phase factor that makes the real-valued adiabatic electronic wave function single-valued, provided that the curl of the derivative coupling is zero. Unfortunately for ab initio determined wave functions, the curl is never rigorously zero. However, when the wave functions are determined from a coupled two diabatic state Hamiltonian H(d) (fit to ab initio data), the resulting derivative couplings are by construction curl free, except at points of conical intersection. In this work we focus on a recently introduced diabatization scheme that produces the H(d) by fitting ab initio determined energies, energy gradients, and derivative couplings to the corresponding H(d) determined quantities in a least squares sense, producing a removable approximation to the ab initio determined derivative coupling. This approach and related numerical issues associated with the nonremovable ab initio derivative couplings are illustrated using a full 33-dimensional representation of phenol photodissociation. The use of this approach to provide a general framework for treating the molecular Aharonov Bohm effect is demonstrated.

    View details for DOI 10.1063/1.4971369

    View details for Web of Science ID 000391688900012

    View details for PubMedID 28010097