most citedA General Framework for Equivariant Neural Networks on Reductive Lie Groups

2 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

physics.comp-ph20242 cited

Surrogate models for vibrational entropy based on a spatial decomposition

Tina Torabi, Yangshuai Wang, Christoph Ortner

The temperature-dependent behavior of defect densities within a crystalline structure is intricately linked to the phenomenon of vibrational entropy. Traditional methods for evalua…

physics.comp-ph20232 cited

A Theoretical Case Study of the Generalisation of Machine-learned Potentials

Yangshuai Wang, Shashwat Patel, Christoph Ortner

Machine-learned interatomic potentials (MLIPs) are typically trained on datasets that encompass a restricted subset of possible input structures, which presents a potential challen…

math-ph2023

Thermodynamic Limits of Electronic Systems

David Gontier, Jianfeng Lu, Christoph Ortner

We review thermodynamic limits and scaling limits of electronic structure models for condensed matter. We discuss several mathematical ways to implement these limits in three model…

physics.comp-ph20232 cited

On the Atomic Cluster Expansion: interatomic potentials and beyond

Christoph Ortner

The Atomic Cluster Expansion (ACE) [R. Drautz, Phys. Rev. B, 99:014104 (2019)] provides a systematically improvable, universal descriptor for the environment of an atom that is inv…

stat.ML20232 cited

A General Framework for Equivariant Neural Networks on Reductive Lie Groups

Ilyes Batatia, Mario Geiger, Jose Munoz +3

Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, or the unitary groups, play essential roles across scientific fields as diverse as high energy physics, quan…

physics.comp-ph20231 cited

A Multilevel Method for Many-Electron Schrödinger Equations Based on the Atomic Cluster Expansion

Dexuan Zhou, Huajie Chen, Cheuk Hin Ho +1

The atomic cluster expansion (ACE) (Drautz, 2019) yields a highly efficient and intepretable parameterisation of symmetric polynomials that has achieved great success in modelling…