SAMOP 2023 –
wissenschaftliches Programm
MO 7: Machine Learning and Computational and Theoretical Molecular Physics
Mittwoch, 8. März 2023, 11:00–13:00, F142
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11:00 |
MO 7.1 |
Hauptvortrag:
Augmenting basis with normalizing flows for solving Schrödinger equations: theoretical analysis — •Yahya Saleh, Armin Iske, Andrey Yachmenev, and Jochen Küpper
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11:30 |
MO 7.2 |
A machine learning full dimensional potential energy surface for AlF-AlF: lifetime of the intermediate complex — Weiqi Wang, •Xiangyue Liu, and Jesús Pérez-Ríos
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11:45 |
MO 7.3 |
Quantum flows neural network for variational solutions of the Schrödinger equation — •Álvaro Fernández, Yahya Saleh, Andrey Yachmenev, Armin Iske, and Jochen Küpper
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12:00 |
MO 7.4 |
Electronic excited states in deep variational Monte Carlo — •Mike Entwistle, Zeno Schätzle, Paolo Erdman, Jan Hermann, and Frank Noé
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12:15 |
MO 7.5 |
The performance of CCSD(T) for the calculation of dipole moments in diatomics — •Xiangyue Liu, Laura McKemmish, and Jesús Pérez-Ríos
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12:30 |
MO 7.6 |
Non-Local Polarizability Density as a Building Block for Dispersion Density Functionals — •Szabolcs Goger, Peter Szabo, Dmitry Fedorov, and Alexandre Tkatchenko
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12:45 |
MO 7.7 |
Few-Body Physics of the Trapped Atoms: The Configuration Interaction Approach — •Matee ur Rehman, Alejandro Saenz, Fabio Revuelta Peña, Paul Winter, and Simon Sala
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