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
15 papers · 2 filters
An orbital-based representation for accurate Quantum Machine Learning
Konstantin Karandashev, O. Anatole von Lilienfeld
We introduce an electronic structure based representation for quantum machine learning (QML) of electronic properties throughout chemical compound space. The representation is cons…
Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System
Julian Arnold, Juan Carlos San Vicente Veliz, Debasish Koner +3
A machine learned (ML) model for predicting product state distributions from specific initial states (state-to-distribution or STD) for reactive atom-diatom collisions is presented…
Double Proton Transfer in Hydrated Formic Acid Dimer: Interplay of Spatial Symmetry and Solvent-Generated Force on Reactivity
Kai Töpfer, Silvan Käser, Markus Meuwly
The double proton transfer (DPT) reaction in hydrated formic acid dimer (FAD) is investigated at molecular-level detail. For this, a global and reactive machine learned (ML) potent…
Transfer Learned Potential Energy Surfaces: Accurate Anharmonic Vibrational Dynamics and Dissociation Energies for the Formic Acid Monomer and Dimer
Silvan Käser, Markus Meuwly
The vibrational dynamics of formic acid monomer (FAM) and dimer (FAD) is investigated from machine-learned potential energy surfaces at the MP2 (PES) and transfer-learn…
Development and characterization of high-frequency sources for supersonic beams of fluorine radicals
Patrik Straňák, Ludger Ploenes, Simon Hofsäss +3
We present and compare two high-pressure, high-frequency electric-discharge sources for the generation of supersonic beams of fluorine radicals. The sources are based on dielectric…
A New Design for a Traveling-Wave Zeeman Decelerator: II. Experiment
Tomislav Damjanovic, Stefan Willitsch, Nicolas Vanhaecke +4
A novel traveling-wave Zeeman decelerator based on a double-helix coil geometry capable of decelerating paramagnetic molecules with high efficiency is presented. Moving magnetic tr…
A New Design for a Traveling-Wave Zeeman Decelerator: I. Theory
Tomislav Damjanovic, Stefan Willitsch, Nicolas Vanhaecke +4
The concept of a novel traveling wave Zeeman deccelerator based on a double-helix wire geometry capable of decelerating paramagnetic species with high efficiency is presented. A mo…
Conformer-specific polar cycloaddition of dibromobutadiene with trapped propene ions
Ardita Kilaj, Jia Wang, Patrik Stranak +6
Diels-Alder cycloadditions are efficient routes for the synthesis of cyclic organic compounds. There has been a long-standing discussion whether these reactions proceed via stepwis…
Thermal and Vibrationally Activated Decomposition of the syn-CHCHOO Criegee Intermediate
Meenu Upadhyay, Markus Meuwly
The full reaction pathway between the syn-CHCHOO Criegee Intermediate via vinyl hydroxyperoxide to OH+CHCOH is followed for vibrationally excited and thermally prepared rea…
A novel crossed-molecular-beam experiment for investigating reactions of state- and conformationally selected strong-field-seeking molecules
L. Ploenes, P. Straňák, H. Gao +2
The structure and quantum state of the reactants have a profound impact on the kinetics and dynamics of chemical reactions. Over the past years, significant advances have been made…
Impact of the characteristics of quantum chemical databases on machine learning predictions of tautomerization energies
Luis Itza Vazquez-Salazar, Eric Boittier, Oliver T. Unke +1
An essential aspect for adequate predictions of chemical properties by machine learning models is the database used for training them. However, studies that analyze how the content…
Genesis of Polyatomic Molecules in Dark Clouds: CO Formation on Cold Amorphous Solid Water
Meenu Upadhyay, Marco Pezzella, Markus Meuwly
Understanding the formation of molecules under conditions relevant to interstellar chemistry is fundamental to characterize the chemical evolution of the universe. Using reactive m…
MP2 Is Not Good Enough: Transfer Learning ML Models for Accurate VPT2 Frequencies
Silvan Käser, Eric Boittier, Meenu Upadhyay +1
The calculation of the anharmonic modes of small to medium sized molecules for assigning experimentally measured frequencies to the corresponding type of molecular motions is compu…
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…
Transformative Applications of Machine Learning for Chemical Reactions
M. Meuwly
Machine learning techniques applied to chemical reactions has a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to platf…