134 citations · 266 across the 10 of their papers we have counts for
11 papers
Recovering molecules from coarse-grained beads: free-energy-conditioned generative backmapping across chemical space
Luis Itza Vazquez-Salazar, Tristan Bereau
Transferable coarse-grained (CG) force fields compress chemical space: by aggregating atoms into a reduced set of interaction beads, models such as MARTINI reduce the number of dis…
Design, Assessment, and Application of Machine Learning Potential Energy Surfaces
Valerii Andreichev, Sena Aydin, Kai Töpfer +2
Potential Energy Surfaces (PESs) are an indispensable tool to investigate, characterise and understand chemical and biological systems in the gas and condensed phases. Advances in…
Augmenting chemical databases for atomistic machine learning by sampling conformational space
Luis Itza Vazquez-Salazar, Markus Meuwly
Machine learning (ML) has become a standard tool for the exploration of chemical space. Much of the performance of such models depends on the chosen database for a given task. Here…
: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces
Kai Töpfer, Luis Itza Vazquez-Salazar, Markus Meuwly
With the establishment of machine learning (ML) techniques in the scientific community, the construction of ML potential energy surfaces (ML-PES) has become a standard process in p…
Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
Luis Itza Vazquez-Salazar, Silvan Käser, Markus Meuwly
Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is applied to reactive molecular potential energy surfaces (PESs). Three methods - Ensembles…
Improving Potential Energy Surfaces Using Experimental Feshbach Resonance Tomography
Karl P. Horn, Luis Itza Vazquez-Salazar, Christiane P. Koch +1
The structure and dynamics of a molecular system is governed by its potential energy surface (PES), representing the total energy as a function of the nuclear coordinates. Obtainin…
PhysNet Meets CHARMM: A Framework for Routine Machine Learning / Molecular Mechanics Simulations
Kaisheng Song, Silvan Käser, Kai Töpfer +2
Full dimensional potential energy surfaces (PESs) based on machine learning (ML) techniques provide means for accurate and efficient molecular simulations in the gas- and condensed…
Neural Network Potentials for Chemistry: Concepts, Applications and Prospects
Silvan Käser, Luis Itza Vazquez-Salazar, Markus Meuwly +1
Artificial Neural Networks (ANN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of poten…
Uncertainty quantification for predictions of atomistic neural networks
Luis Itza Vazquez-Salazar, Eric D. Boittier, M. Meuwly
The value of uncertainty quantification on predictions for trained neural networks (NNs) on quantum chemical reference data is quantitatively explored. For this, the architecture o…
Structure, Organization and Heterogeneity of Water-Containing Deep Eutectic Solvents
Kai Töpfer, Andrea Pasti, Anuradha Das +6
The spectroscopy and structural dynamics of a deep eutectic mixture (KSCN/acetamide) with varying water content is investigated from 2D IR (with the C-N stretch vibration of the SC…
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…