Veronika Jurásková

Mansfield University — United States

Computer Science · Physical Sciences

8h-index213citations26works6.12yr avg
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Research Topics

Computational Drug Discovery Methods(11), Machine Learning in Materials Science(11), Various Chemistry Research Topics(4), Protein Structure and Dynamics(3), Crystallography and molecular interactions(3)

Publications26 total

Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentials
Digital Discovery·2025· 6 citedOpen Access
Author response for "Active learning meets metadynamics: Automated workflow for reactive machine learning interatomic potentials"
2025
Author response for "Active learning meets metadynamics: Automated workflow for reactive machine learning interatomic potentials"
2025
Author response for "Active learning meets metadynamics: Automated workflow for reactive machine learning interatomic potentials"
2025
Distillation of atomistic foundation models across architectures and chemical domains
ArXiv.org·2025· 2 citedOpen Access
Active learning meets metadynamics: Automated workflow for reactive machine learning potentials
2025· 3 citedOpen Access
Discovering chemical structure: general discussion
Faraday Discussions·2024Open Access
Capturing Dichotomic Solvent Behavior in Solute–Solvent Reactions with Neural Network Potentials
Journal of Chemical Theory and Computation·2024· 8 citedOpen Access
Active learning meets metadynamics: Automated workflow for reactive machine learning potentials
2024· 2 citedOpen Access
Capturing Dichotomic Solvent Behavior in Solute--Solvent Reactions with Neural Network Potentials
2024
Modelling ligand exchange in metal complexes with machine learning potentials
Faraday Discussions·2024· 8 citedOpen Access
Modelling chemical processes in explicit solvents with machine learning potentials
Nature Communications·2024· 59 citedOpen Access
Modeling Chemical Processes in Explicit Solvents with Machine Learning Potentials
2024· 1 citedOpen Access
Modelling ligand exchange in metal complexes with machine learning potentials
2024Open Access
From Organic Fragments to Photoswitchable Catalysts: The OFF–ON Structural Repository for Transferable Kernel-Based Potentials
Journal of Chemical Information and Modeling·2024· 2 citedOpen Access
From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-based Potentials
2023· 1 citedOpen Access
Modeling Chemical Processes in Explicit Solvents with Machine Learning Potentials
2023Open Access
How Robust Is the Reversible Steric Shielding Strategy for Photoswitchable Organocatalysts?
The Journal of Organic Chemistry·2022· 8 citedOpen Access
Assessing the persistence of chalcogen bonds in solution with neural network potentials
The Journal of Chemical Physics·2022· 15 citedOpen Access
Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides
Journal of Chemical Theory and Computation·2022· 15 citedOpen Access
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Frequent Co-authors

Fernanda Duarte(12), Hanwen Zhang(9), Clémence Corminbœuf(8), Valdas Vitartas(6), Tristan Johnston-Wood(6), Frédéric Célerse(5), Raimón Fabregat(4), Matthew D. Wodrich(4), Simone Gallarati(3), Michele Ceriotti(2), Shubhajit Das(2), Gers Tusha(2), Lars V. Schäfer(2), Sergi Vela(2), Chris J. Pickard(2), Volker L. Deringer(2), Kevin Rossi(1), Raphaël Wischert(1), Laurent Garel(1), Jiří Vymětal(1)