9 citations · 19 across the 5 of their papers we have counts for
5 papers
A New Paradigm for Computational Chemistry
Raphael T. Husistein, Markus Reiher
Computational chemistry has become an indispensable tool for generating data and insights, pervading all branches of experimental chemistry. Its most central concept is the potenti…
How to use quantum computers for biomolecular free energies
Jakob Günther, Thomas Weymuth, Moritz Bensberg +18
Free energy calculations are at the heart of physics-based analyses of biochemical processes. They allow us to quantify molecular recognition mechanisms, which determine a wide ran…
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…
Hierarchical quantum embedding by machine learning for large molecular assemblies
Moritz Bensberg, Marco Eckhoff, Raphael T. Husistein +9
We present a quantum-in-quantum embedding strategy coupled to machine learning potentials to improve on the accuracy of quantum-classical hybrid models for the description of large…
NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance
Raphael T. Husistein, Markus Reiher, Marco Eckhoff
Artificial neural networks have been shown to be state-of-the-art machine learning models in a wide variety of applications, including natural language processing and image recogni…