27 citations · 27 across the 1 of their papers we have counts for
5 papers
Machine learning based energy-free structure predictions of molecules (closed and open-shell), transition states, and solids
Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld
The computational prediction of atomistic structure is a long-standing problem in physics, chemistry, materials, and biology. Within conventional force-field or {\em ab initio} cal…
Effects of perturbation order and basis set on alchemical predictions
Giorgio Domenichini, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Alchemical perturbation density functional theory has been shown to be an efficient and computationally inexpensive way to explore chemical compound space. We investigate approxima…
Rapid and accurate molecular deprotonation energies from quantum alchemy
Guido Falk von Rudorff, O. Anatole von Lilienfeld
We assess the applicability of Alchemical Perturbation Density Functional Theory (APDFT) for quickly and accurately estimating deprotonation energies. We have considered all possib…
Machine learning the computational cost of quantum chemistry
Stefan Heinen, Max Schwilk, Guido Falk von Rudorff +1
Computational quantum mechanics based molecular and materials design campaigns consume increasingly more high-performance compute resources, making improved job scheduling efficien…
Alchemical perturbation density functional theory (APDFT)
Guido Falk von Rudorff, O. Anatole von Lilienfeld
We introduce an orbital free electron density functional approximation based on alchemical perturbation theory. Given convergent perturbations of a suitable reference system, the a…