3 papers
physics.chem-ph2025
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…
physics.chem-ph2025
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…
physics.chem-ph2024
: 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…