20 citations · 138 across the 26 of their papers we have counts for
16 papers · 1 filter
OrbGNN: A Wave function-based Machine Learning Interelectronic Representation
Brody Quebedeaux, Shahzad Akram, Markus Reiher +1
Machine learning interatomic potentials (MLIPs) have become emerging tools in molecular modeling and computational chemistry. By learning high-dimensional potential energy surfaces…
General Symmetry-Based Potential Energy Surface Grid Reduction in Normal Coordinates
Can Liao, Markus Reiher
The construction of a grid-based potential energy surface (PES) can be prohibitively expensive as the number of grid points grows exponentially with molecular size. Molecular symme…
N-Mode Quantized Anharmonic Vibronic Hamiltonians for Matrix Product State Dynamics
Valentin Barandun, Nina Glaser, Markus Reiher
Theoretical predictions of photochemical processes are essential for interpreting and understanding spectral features. Reliable quantum dynamics calculations of vibronic systems re…
Efficient Implementation of the Spin-Free Renormalized Internally-Contracted Multireference Coupled Cluster Theory
Kalman Szenes, Riya Kayal, Kantharuban Sivalingam +3
In this paper, an efficient implementation of the renormalized internally-contracted multreference coupled cluster with singles and doubles (RIC-MRCCSD) into the ORCA quantum chemi…
Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations
Marco Eckhoff, Markus Reiher
Recent developments in computational chemistry facilitate the automated quantum chemical exploration of chemical reaction networks for the in-silico prediction of synthesis pathway…
Machine Learning Enhanced Calculation of Quantum-Classical Binding Free Energies
Moritz Bensberg, Marco Eckhoff, F. Emil Thomasen +10
Binding free energies are a key element in understanding and predicting the strength of protein--drug interactions. While classical free energy simulations yield good results for m…