Search Results for author:
Found 4 papers, 4 papers with code
Probing the effects of broken symmetries in machine learning
We focus specifically on physical observables that are likely to be affected -- directly or indirectly -- by symmetry breaking, finding negligible consequences when the model is used in an interpolative, bulk, regime.
Stress and heat flux via automatic differentiation
Machine-learning potentials provide computationally efficient and accurate approximations of the Born-Oppenheimer potential energy surface.
Heat flux for semi-local machine-learning potentials
The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials.
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise simulations.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
