16 citations · 17 across the 6 of their papers we have counts for
6 papers
Orbital-Free Surrogate Functionals Yield Transferable Interatomic Potentials and Electron Densities
Simon Wagner, Marc K. Ickler, Manuel V. Klockow +2
Orbital-free density functional theory seeks to compute the energy of an electronic system directly from its electron density, avoiding one-electron wave functions and thereby offe…
Surrogate Functionals for Machine-Learned Orbital-Free Density Functional Theory
Roman Remme, Fred A. Hamprecht
We introduce surrogate functionals: machine-learned energy functionals for orbital-free density functional theory (OF-DFT) which are defined not by universal fidelity to a physical…
Stable and Accurate Orbital-Free DFT Powered by Machine Learning
Roman Remme, Tobias Kaczun, Tim Ebert +10
Hohenberg and Kohn have proven that the electronic energy and the one-particle electron density can, in principle, be obtained by minimizing an energy functional with respect to th…
Beyond Canonicalization: How Tensorial Messages Improve Equivariant Message Passing
Peter Lippmann, Gerrit Gerhartz, Roman Remme +1
In numerous applications of geometric deep learning, the studied systems exhibit spatial symmetries and it is desirable to enforce these. For the symmetry of global rotations and r…
KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory
Roman Remme, Tobias Kaczun, Maximilian Scheurer +2
Orbital-free density functional theory (OF-DFT) holds the promise to compute ground state molecular properties at minimal cost. However, it has been held back by our inability to c…
Learning the Arrow of Time
Nasim Rahaman, Steffen Wolf, Anirudh Goyal +2
We humans seem to have an innate understanding of the asymmetric progression of time, which we use to efficiently and safely perceive and manipulate our environment. Drawing inspir…