Deep learning approaches for jet tagging in high-energy physics are
char...
Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, ...
The atomic cluster expansion (ACE) (Drautz, 2019) yields a highly effici...
Lattice defects in crystalline materials create long-range elastic field...
Hybrid quantum/molecular mechanics models (QM/MM methods) are widely use...
Data-driven interatomic potentials have emerged as a powerful class of
s...
Density based representations of atomic environments that are invariant ...
Machine-learned interatomic potentials (MLIPs) and force fields (i.e.
in...
Deep Learning approaches are becoming the go-to methods for data analysi...
Creating fast and accurate force fields is a long-standing challenge in
...
The rapid progress of machine learning interatomic potentials over the p...
The minimum energy path (MEP) describes the mechanism of reaction, and t...
The minimum energy path (MEP) is the most probable transition path that
...
We present a comprehensive analysis of an algorithm for evaluating
high-...
We study the polynomial approximation of symmetric multivariate function...
We develop and analyze a framework for consistent QM/MM (quantum/classic...
We investigate the convergence of the Crouzeix-Raviart finite element me...
Hybrid quantum/molecular mechanics models (QM/MM methods) are widely use...
In this note we detail a framework to systematically derive polynomial b...
This paper presents a unified approach to the modeling and computation o...