In the past decade, we have developed methods to extract chemically intuitive information from numerical calculations based on many-particle quantum physics. These efforts include the extraction of molecular structure from pre-Born-Oppenheimer calculations [1], the selective calculation of only desired molecular vibrations [2], the focus on relevant parts of the Hilbert space by tensor decomposition [3], and the ultrafast calculation of atomic forces in real-time [4], which makes parametrizations of interaction potentials based on machine learning possible.
In my talk, I will give an overview on these developments and discuss open problems and challenges.
[1] E. Matyus, J. Hutter, U. Mueller-Herold, M. Reiher, J. Chem. Phys. 135, 204302 (2011).
[2] C. Herrmann, J. Neugebauer, M. Reiher, New J. Chem. 31, 818 (2007).
[3] K. H. Marti, M. Reiher, Phys. Chem. Chem. Phys. 13, 6750 (2011).
[4] M. P. Haag, Int. J. Quantum. Chem. 113, 8 (2013).
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