SAMOP 2023 – wissenschaftliches Programm
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MO: Fachverband Molekülphysik
MO 7: Machine Learning and Computational and Theoretical Molecular Physics
MO 7.3: Vortrag
Mittwoch, 8. März 2023, 11:45–12:00, F142
Quantum flows neural network for variational solutions of the Schrödinger equation — •Álvaro Fernández1,3, Yahya Saleh1,4, Andrey Yachmenev1,2, Armin Iske4, and Jochen Küpper1,2,3 — 1Center for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron DESY, Hamburg — 2Center for Ultrafast Imaging, Universität Hamburg — 3Department of Physics, Universität Hamburg — 4Department of Mathematics, Universität Hamburg
Recently, a few deep neural network models for solving the electronic Schrödinger equation were developed, demonstrating both outstanding computing efficiency and accurate results.
Here, we present a new quantum-flow-neural-network approach for obtaining variational solutions of the stationary Schrödinger equation. At the core of the method is an invertible neural network composed with the general basis of orthogonal functions, which provides a more stable framework for simultaneous optimization of the ground state and excited states. This approach is applied in calculations of the vibrational energy levels of polyatomic molecules as well as of electronic energies in a single-active-electron approximation. The results show a considerable improvement of variational convergence for the ground and the excited states. In addition, we extend our approach for solving the time dependent problems using recurrent flows.