245 citations · 579 across the 21 of their papers we have counts for
23 papers
Representative Random Sampling of Chemical Space
Diego J. Monterrubio-Chanca, Guido Falk von Rudorff
The overwhelming majority of molecules remains unexplored. This is mostly due to the sheer number of them, which prohibits any enumeration of chemical space, the set of all such mo…
Intrinsic Dimensionality of Molecular Properties
Ali Banjafar, Guido Falk von Rudorff
Chemical space which encompasses all stable compounds is unfathomably large and its dimension scales linearly with the number of atoms considered. The success of machine learning m…
Machine Learning Conservation Laws of Dynamical systems
Meskerem Abebaw Mebratie, Rüdiger Nather, Guido Falk von Rudorff +1
Conservation laws are of great theoretical and practical interest. We describe a novel approach to machine learning conservation laws of finite-dimensional dynamical systems using…
Quantum mechanical dataset of 836k neutral closed shell molecules with upto 5 heavy atoms from CNOFSiPSClBr
Danish Khan, Anouar Benali, Scott Y. H. Kim +2
We introduce the Vector-QM24 (VQM24) dataset comprehensively covering all possible neutral closed-shell small organic and inorganic molecules with up to five heavy (\textit{p}-bloc…
Transferability of atomic energies from alchemical decomposition
Michael J. Sahre, Guido Falk von Rudorff, Philipp Marquetand +1
We study alchemical atomic energy partitioning as a method to estimate atomisation energies from atomic contributions which are defined in physically rigorous and general ways thro…
Exact sampling of molecules in chemical space
Jan Weinreich, Konstantin Karandashev, Guido Falk von Rudorff
The concept of molecular similarity appears in many machine-learning algorithms based on the assumption that molecules with similar representations will also share similar properti…