Michele Ceriotti

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Matthias Rupp

Machine Learning for Quantum Mechanics

Data-driven surrogate models of quantum-mechanical observables have seen increasing success over the last decades. I will highlight some research directions related to Anatole von Lilienfeld's work, including models of potential energy surfaces across chemical space. I will then focus on machine-learning interatomic potentials and their trade-offs, showcasing ultra-fast potentials and their applications as an example.

Joshua Schrier

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Yousung Jung

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Markus Reiher

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Guido von Rudorff

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Mark Tuckerman

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Markus Meuwly

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Tristan Bereau

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Jochen Blumberger

Adventures in Non-adiabatic Dynamics

Scale-bridging techniques have revolutionized our understanding of condensed phase system but these methods are typically confined to the electronic ground state. In my talk I will describe our 10-year long adventure in developing a beyond Born-Oppenheimer (i.e. non-adiabatic) dynamics approach enabling the simulation of electronically excited processes in truly nanoscale molecular systems (10-100 nanometers) and over long time scales (10-100 ps) using coarse grained electronic Hamiltonians. The method will be illustrated with applications to charge transport, excitation energy transport, light-induced charge generation and thermoelectricity generation in organic semiconducting materials.

Daniel Sebastiani

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Enrico Tapavicza

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Clémence Corminboeuf

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John Keith

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Denis Andrienko

Tensorial Molecular Property Predictions with Equivariant GNNs

We propose a first-principles-based parameterization procedure for atomic polarizability tensors and scalars, validated on a set of small organic molecules with conjugated building blocks. To overcome the computational cost of ab initio calculations, we train a message-passing graph neural network to predict polarizability parameters, enabling efficient and scalable parameterization. This approach imposes no additional computational cost during simulations and provides a clear diagnostic criterion for identifying cases where polarizable force-field models fail to accurately describe molecular polarizability.