596 citations · 716 across the 8 of their papers we have counts for
9 papers
Energy dissipation at the atomic scale explains how fracture energy depends on crack velocity in silica glass
Marthe Grønlie Guren, Sigbjørn Løland Bore, François Renard +1
The fracture energy of brittle materials rises with crack velocity, and this effect is typically attributed to surface roughening from path instabilities. Here we show, using molec…
Why ice is so slippery
Sigbjørn Løland Bore, B. N. J. Persson, Henrik Andersen Sveinsson
We simulate nanoscale ice--glass friction from first principles and upscale it by modelling frictional heating. Nanoscale simulations alone overestimate friction and miss its veloc…
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Jinzhe Zeng, Duo Zhang, Anyang Peng +44
In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for m…
Learning atomic forces from uncertainty-calibrated adversarial attacks
Henrique Musseli Cezar, Tilmann Bodenstein, Henrik Andersen Sveinsson +3
Adversarial approaches, which intentionally challenge machine learning models by generating difficult examples, are increasingly being adopted to improve machine learning interatom…
DeePMD-kit v2: A software package for Deep Potential models
Jinzhe Zeng, Duo Zhang, Denghui Lu +44
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. T…
On the equivalence of the hybrid particle-field and Gaussian core models
Morten Ledum, Samiran Sen, Sigbjørn Løland Bore +1
Hybrid particle-field molecular dynamics is a molecular simulation strategy wherein particles couple to a density field instead of through ordinary pair potentials. Traditionally c…