2 citations · 9 across the 6 of their papers we have counts for
5 papers · 1 filter
Self-consistent Coulomb interactions for machine learning interatomic potentials
Jack Thomas, William J. Baldwin, Gábor Csányi +1
A ubiquitous approach to obtain transferable machine learning-based models of potential energy surfaces for atomistic systems is to decompose the total energy into a sum of local a…
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…
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…
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…
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…