6.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR336 papers
- ETH ZurichCH215 papers
- Petersburg Nuclear Physics InstituteRU204 papers
- Massachusetts Institute of TechnologyUS182 papers
- European Organization for Nuclear ResearchCH168 papers
- Institut National de Physique Nucléaire et de Physique des ParticulesFR167 papers
- University of GenevaCH163 papers
- Hungarian Academy of SciencesHU159 papers
- California Institute of TechnologyUS145 papers
- Paul Scherrer InstituteCH143 papers
- University of BolognaIT143 papers
- University of MichiganUS143 papers
18 papers · 2 filters
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…
Neural networks and kernel ridge regression for excited states dynamics of CHNH: From single-state to multi-state representations and multi-property machine learning models
Julia Westermayr, Felix A. Faber, Anders S. Christensen +2
Excited-state dynamics simulations are a powerful tool to investigate photo-induced reactions of molecules and materials and provide complementary information to experiments. Since…
Formation and Stabilization of Ground and Excited State Singlet O upon Recombination of P Oxygen on Amorphous Solid Water
Marco Pezzella, Debasish Koner, Markus Meuwly
The recombination dynamics of P oxygen atoms on cold amorphous solid water to form triplet and singlet molecular oxygen (O) is followed under conditions representative for…
Reactive Dynamics and Spectroscopy of Hydrogen Transfer from Neural Network-Based Reactive Potential Energy Surfaces
Silvan Käser, Oliver T. Unke, Markus Meuwly
The in silico exploration of chemical, physical and biological systems requires accurate and efficient energy functions to follow their nuclear dynamics at a molecular and atomisti…
The Reaction: Thermal and Vibrational Relaxation Rates for the A, A and A States
Juan Carlos San Vicente Veliz, Debasish Koner, Max Schwilk +2
The kinetics and vibrational relaxation of the reaction is investigated over a wide temperature ra…
Rotational state-changing collisions between N and Rb at low energies
A. D. Dörfler, E. Yurtsever, P. Villarreal +3
We present a theoretical study of rotationally elastic and inelastic collisions between molecular nitrogen ions and Rb atoms in the sub-Kelvin temperature regime prevalent in ion-a…
High-Dimensional Potential Energy Surfaces for Molecular Simulations
Oliver T. Unke, Debasish Koner, Sarbani Patra +2
An overview of computational methods to describe high-dimensional potential energy surfaces suitable for atomistic simulations is given. Particular emphasis is put on accuracy, com…
Near dissociation states for H-He on MRCI and FCI potential energy surfaces
Debasish Koner, Juan Carlos San Vicente Veliz, Ad van der Avoird +1
A new analytical potential energy surface (PES) has been constructed for H-He using a reproducing kernel Hilbert space (RKHS) representation from an extensive number of $ab i…
Machine Learning Potential Energy Surfaces
Oliver T. Unke, Markus Meuwly
Machine Learning techniques can be used to represent high-dimensional potential energy surfaces for reactive chemical systems. Two such methods are based on a reproducing kernel Hi…
FCHL revisited: faster and more accurate quantum machine learning
Anders S. Christensen, Lars A. Bratholm, Felix A. Faber +1
We introduce the FCHL19 representation for atomic environments in molecules or condensed-phase systems. Machine learning models based on FCHL19 are able to yield predictions of ato…
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…
Atoms in molecules from alchemical perturbation density functional theory
Guido Falk von Rudorff, O. Anatole von Lilienfeld
Based on thermodynamic integration we introduce atoms in molecules (AIM) using the orbital-free framework of alchemical perturbation density functional theory (APDFT). Within APDFT…
Long-range vs. short-range effects in cold molecular ion-neutral collisions: Charge exchange of Rb with N and O
Alexander D. Dörfler, Pascal Eberle, Debasish Koner +3
We report a study of cold charge-transfer (CT) collisions of Rb atoms with N and O ions in the mK regime using a dynamic ion-neutral hybrid trapping experiment. State-…
Reactive Atomistic Simulations of Diels-Alder Reactions: the Importance of Molecular Rotations
Uxía Rivero, Oliver T. Unke, Markus Meuwly +1
The Diels-Alder reaction between 2,3-dibromo-1,3-butadiene and maleic anhydride has been studied by means of multisurface adiabatic reactive molecular dynamics and the PhysNet neur…
Molecular shape as a (useful) bias in chemistry
Guido Falk von Rudorff
One of the molecular properties most intuitive to the human perception is the geometrical shape. However, when exploring a large chemical space the determination of shape needs to…
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
Oliver T. Unke, Markus Meuwly
In recent years, machine learning (ML) methods have become increasingly popular in computational chemistry. After being trained on appropriate ab initio reference data, these metho…
Solvent-aware Interfaces in Continuum Solvation
Oliviero Andreussi, Nicolas Georg Hörmann, Francesco Nattino +3
Continuum models to handle solvent and electrolyte effects in an effective way have a long tradition in quantum-chemistry simulations and are nowadays also being introduced in comp…
A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules
Lixue Cheng, Matthew Welborn, Anders S. Christensen +1
We address the degree to which machine learning can be used to accurately and transferably predict post-Hartree-Fock correlation energies. Refined strategies for feature design and…