Efficiency Considerations in Solving Smoluchowski Equations for Rough Potentials. - Polina V. Banushkina; Olaf Schenk; Markus Meuwly (CompLife, 2005)

Reactions in complex biologically relevant systems: challenges for computational approaches. - Markus Meuwly (Future Gener. Comput. Syst., 2005)

Importance of individual side chains for the stability of a protein fold: Computational alanine scanning of the insulin monomer. - Vincent Zoete; Markus Meuwly (J. Comput. Chem., 2006)

Reactive force fields for proton transfer dynamics. - Sven Lammers; Stephan Lutz; Markus Meuwly (J. Comput. Chem., 2008)

Force Field Optimization using Dynamics and Ensemble Averaged Data: Vibrational Spectra and Relaxation in Bound MbCO. - Michael Devereux; Markus Meuwly (J. Chem. Inf. Model., 2010)

Atomic multipoles: Electrostatic potential fit, local reference axis systems, and conformational dependence. - Christian Kramer; Peter Gedeck; Markus Meuwly (J. Comput. Chem., 2012)

Deriving Static Atomic Multipoles from the Electrostatic Potential. - Christian Kramer; Tristan Bereau; Alexander Spinn; Klaus R. Liedl; Peter Gedeck; Markus Meuwly (J. Chem. Inf. Model., 2013)

A supervised fitting approach to force field parametrization with application to the SIBFA polarizable force field. - Michael Devereux; Nohad Gresh; Jean-Philip Piquemal; Markus Meuwly (J. Comput. Chem., 2014)

Toward force fields for atomistic simulations of iridium-containing complexes. - Franziska D. Hofmann; Michael Devereux; Andreas Pfaltz; Markus Meuwly (J. Comput. Chem., 2014)

Quantum-chemistry based calibration of the alkali metal cation series (Li+-Cs+) for large-scale polarizable molecular mechanics/dynamics simulations. - Todor Dudev; Mike Devereux; Markus Meuwly; Carmay Lim; Jean-Philip Piquemal; Nohad Gresh (J. Comput. Chem., 2015)

A Toolkit to Fit Nonbonded Parameters from and for Condensed Phase Simulations. - Florent Hédin; Krystel El Hage; Markus Meuwly (J. Chem. Inf. Model., 2016)

Correction to A Toolkit to Fit Nonbonded Parameters from and for Condensed Phase Simulations. - Florent Hédin; Krystel El Hage; Markus Meuwly (J. Chem. Inf. Model., 2017)

Migration of small ligands in globins: Xe diffusion in truncated hemoglobin N. - Polydefkis Diamantis; Oliver T. Unke; Markus Meuwly (PLoS Comput. Biol., 2017)

Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces. - Oliver T. Unke; Markus Meuwly (J. Chem. Inf. Model., 2017)

High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning. - Oliver T. Unke; Debasish Koner; Sarbani Patra; Silvan Käser; Markus Meuwly (Mach. Learn. Sci. Technol., 2020)

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; Narendra Singh; Raymond J. Bemish; Markus Meuwly (CoRR, 2021)

Atomistic Simulations for Reactions and Spectroscopy in the Era of Machine Learning - Quo Vadis? - Markus Meuwly (CoRR, 2022)

Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces. - Luis Itza Vazquez-Salazar; Silvan Käser; Markus Meuwly (CoRR, 2024)

Asparagus: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces. - Kai Töpfer; Luis Itza Vazquez-Salazar; Markus Meuwly (CoRR, 2024)

The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks. - Silvan Käser; Debasish Koner; Markus Meuwly (CoRR, 2024)

Systematic improvement of empirical energy functions in the era of machine learning. - Mike Devereux; Eric D. Boittier; Markus Meuwly (J. Comput. Chem., 2024)

Asparagus: A toolkit for autonomous, user-guided construction of machine-learned potential energy surfaces. - Kai Töpfer; Luis Itza Vazquez-Salazar; Markus Meuwly (Comput. Phys. Commun., 2025)

Augmenting Chemical Databases for Atomistic Machine Learning by Sampling Conformational Space. - Luis Itza Vazquez-Salazar; Markus Meuwly (J. Chem. Inf. Model., 2025)