Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

CPP: Fachverband Chemische Physik und Polymerphysik

CPP 23: Modeling and Simulation of Soft Matter III

CPP 23.6: Vortrag

Mittwoch, 20. März 2024, 10:45–11:00, H 0107

Long-Range Descriptors in Atomistic Modeling beyond Electrostatics — •Philip Loche, Kevin Kazuki Huguenin-Dumittan, and Michele Ceriotti — Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, Switzerland

Over the last decade, the use of machine learning based methods for modelling materials and molecules has developed rapidly. A key ingredient in most successful approaches has been the use of locality, also termed "nearsightedness" of electronic models. Local models truncate atomic interactions beyond some cutoff radius which allows the development of fast algorithms scaling linearly with the number of atoms. What is systematically neglected in these models are any kind of long-range interactions interactions, including electrostatics, dipole-dipole or van der Waals forces, due to the relatively high computational cost involved.

We present a general mathematical framework that is an extension of the long-range equivariant (LODE) descriptor, and can describe all long-range interactions with an inverse power law form. It can be used to predict scalar target properties (energies) but can also be used in models that need to predict gradients of those (forces). We illustrate how this extension leads to significant improvements in both accuracy and computational cost.

Keywords: machine learning; long-range; many-body; electrostatics