20 citations · 44 across the 6 of their papers we have counts for
6 papers
Machine learning predictions of the Hessian matrix for peptides chains and small proteins
Giorgio Domenichini
Molecular Hessians have a key role in describing molecular vibrations, trajectories and optimization paths. An explicit calculation of them through standard quantum mechanical meth…
Extending the definition of atomic basis sets to atoms with fractional nuclear charge
Giorgio Domenichini
Alchemical transformations showed that perturbation theory can be applied also to changes in the atomic nuclear charges of a molecule. The alchemical path that connects two differe…
Molecular Hessian matrices from a machine learning random forest regression algorithm
Giorgio Domenichini, Christoph Dellago
In this article we present a machine learning model to obtain fast and accurate estimates of the molecular Hessian matrix. In this model, based on a random forest, the second deriv…
Alchemical diastereomers from antisymmetric alchemical perturbations
O. Anatole von Lilienfeld, Giorgio Domenichini
The energy difference between two iso-electronic systems can be accurately approximated by the alchemical first order Hellmann-Feynmann derivative for the averaged Hamiltonian. Thi…
Alchemical geometry relaxation
Giorgio Domenichini, O. Anatole von Lilienfeld
We propose to relax geometries throughout chemical compound space (CCS) using alchemical perturbation density functional theory (APDFT). APDFT refers to perturbation theory involvi…
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…