519 citations · 3k across the 38 of their papers we have counts for
32 papers · 1 filter
Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials
Maximilian X. Tiefenbacher, Brigitta Bachmair, Cheng Giuseppe Chen +4
Excited-state nonadiabatic simulations with quantum mechanics/molecular mechanics (QM/MM) are essential to understand photoinduced processes in explicit environments. However, the…
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…
Nonadiabatic forward flux sampling for excited-state rare events
Madlen Maria Reiner, Brigitta Bachmair, Maximilian Xaver Tiefenbacher +4
We present a rare event sampling scheme applicable to coupled electronic excited states. In particular, we extend the forward flux sampling (FFS) method for rare event sampling to…
Nonadiabatic ab initio molecular dynamics including spin-orbit coupling and laser fields
Philipp Marquetand, Martin Richter, Jesús González-Vázquez +2
Nonadiabatic ab initio molecular dynamics (MD) including spin-orbit coupling (SOC) and laser fields is investigated as a general tool for studies of excited-state processes. Up to…
BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network / Molecular Mechanics Simulations
Bettina Lier, Peter Poliak, Philipp Marquetand +2
Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have advanced the field of computational chemistry tremendously. However, they require the partitioning of a system…
Roaming leads to amino acid photodamage: A deep learning study of tyrosine
Julia Westermayr, Michael Gastegger, Dora Vörös +4
Although the amino acid tyrosine is among the main building blocks of life, its photochemistry is not fully understood. Traditional theoretical simulations are neither accurate eno…
Additive polarizabilities in ionic liquids
Carlos ES Bernardes, Karina Shimizu, José Nuno Canongia Lopes +4
An extended Designed regression analysis of experimental data on density and refractive indices of several classes of ionic liquids yielded statistically averaged atomic volumes an…
Stark control of a chiral fluoroethylene derivative
Daniel Kinzel, Philipp Marquetand, Leticia González
Hydrogen dissociation is an unwanted competing pathway if a torsional motion around the C=C double bond in a chiral fluoroethylene derivative, namely (4-methylcyclohexylidene) fluo…
A singlet and triplet excited-state dynamics study of the keto and enol tautomers of cytosine
Sebastian Mai, Philipp Marquetand, Martin Richter +2
The photoinduced excited-state dynamics of the keto and enol forms of cytosine is investigated using ab initio surface hopping in order to understand the outcome of molecular beam…
Comparing the Accuracy of High-Dimensional Neural Network Potentials and the Systematic Molecular Fragmentation Method: A Benchmark Study for all-trans Alkanes
Michael Gastegger, Clemens Kauffmann, Jörg Behler +1
Many approaches, which have been developed to express the potential energy of large systems, exploit the locality of the atomic interactions. A prominent example are fragmentation…
Femtosecond Intersystem Crossing in the DNA Nucleobase Cytosine
Martin Richter, Philipp Marquetand, Jesús González-Vazquez +2
Ab initio molecular dynamics including non-adiabatic and spin-orbit couplings on equal footing is used to unravel the deactivation of cytosine after UV light absorption. Intersyste…
Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space
Julia Westermayr, Philipp Marquetand
Machine learning (ML) has shown to advance the research field of quantum chemistry in almost any possible direction and has recently also entered the excited states to investigate…
Machine learning for electronically excited states of molecules
Julia Westermayr, Philipp Marquetand
Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical des…
Machine learning and excited-state molecular dynamics
Julia Westermayr, Philipp Marquetand
Machine learning is employed at an increasing rate in the research field of quantum chemistry. While the majority of approaches target the investigation of chemical systems in thei…
Photoinduced ultrafast dynamics and control of chemical reactions: from quantum to classical dynamics
Leticia González, Philipp Marquetand
This perspective on laser control provides a quick overview over different schemes for coherent control of chemical reactions and photophysical processes. It originally appeared in…
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
Julia Westermayr, Michael Gastegger, Philipp Marquetand
In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advan…
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…
Internal conversion and intersystem crossing pathways in UV excited, isolated uracils and their implications in prebiotic chemistry
Hui Yu, Jose A. Sanchez-Rodriguez, Marvin Pollum +5
The photodynamic properties of molecules determine their ability to survive in harsh radiation environments. As such, the photostability of heterocyclic aromatic compounds to elect…
Ab Initio Molecular Dynamics Relaxation and Intersystem Crossing Mechanisms of 5-Azacytosine
Antonio Carlos Borin, Sebastian Mai, Philipp Marquetand +1
The gas phase relaxation dynamics of photoexcited 5-azacytosine has been investigated by means of SHARC (surface-hopping including arbitrary couplings) molecular dynamics, based on…
Molecular Dynamics with Neural-Network Potentials
Michael Gastegger, Philipp Marquetand
Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time. However, these simulations are inherently held back either by the…
WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
Michael Gastegger, Ludwig Schwiedrzik, Marius Bittermann +2
We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies o…
Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
Michael Gastegger, Jörg Behler, Philipp Marquetand
Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unp…
Ultrafast Intersystem Crossing in SO and Nucleobases
Sebastian Mai, Martin Richter, Philipp Marquetand +1
Mixed quantum-classical dynamics simulations show that intersystem crossing between singlet and triplet states in SO and in nucleobases takes place on an ultrafast timescale (f…
Excitation of Nucleobases from a Computational Perspective II: Dynamics
Sebastian Mai, Martin Richter, Philipp Marquetand +1
This Chapter is devoted to unravel the relaxation processes taking place after photoexcitation of isolated DNA/RNA nucleobases in gas phase from a time-dependent perspective. To th…