Design, Assessment, and Application of Machine Learning Potential Energy Surfaces
Meuwly Group, University of Basel
The present repository provides access to the raw data and potential energy surfaces for AKA tripeptide and DNA base pairs, which are described in detail in Reference [1]. The PESs are obtained following a rational procedure and are based on PhysNet [2]. This repository contains a description of the different PESs, models and corresponding raw data. This is followed by examples on using the neural network-based PESs. The ab initio raw data is available in data/AKA and data/DNA. AKA PES could be found in models/AKA/ directory and DNA PESs (PBE and MP2) could be found in models/DNA/PBE and models/DNA/MP2 respectively.
Most Python scripts that are used to evaluate the PhysNet PESs make use of the atomic simulation environment (ASE) [3] and are written in Python. It is important to get used to ASE, which has very good tutorials online (https://wiki.fysik.dtu.dk/ase/tutorials/tutorials.html#ase). Scripts on how to use the PESs that have been used throughout the evalulation of Reference [1] are given in the evaluation folder. These can for example be used to
When using the PhysNet or the AKA and DNA PESs, please cite the following papers:
Oliver T. Unke and Markus Meuwly "PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges", J. Chem. Theory Comput., 2019, 15, 6, 3678–3693
Andreichev, Valerii, et al. "Design, Assessment, and Application of Machine Learning Potential Energy Surfaces" arXiv preprint https://doi.org/10.48550/arXiv.2511.00951
[1] Andreichev, Valerii, et al. "Design, Assessment, and Application of Machine Learning Potential Energy Surfaces" arXiv preprint https://doi.org/10.48550/arXiv.2511.00951
[2] Oliver T. Unke, and Markus Meuwly "PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges" J. Chem. Theory Comput. 2019, 15, 6, 3678–3693
[3] Ask Hjorth Larsen et al, "The atomic simulation environment—a Python library for working with atoms", 2017, J. Phys.: Condens. Matter, 29, 273002, DOI 10.1088/1361-648X/aa680e
If you have any questions about the codes feel free to contact Valerii Andreichev (valerii.andreichev@unibas.ch) or Prof. Markus Meuwly (m.meuwly@unibas.ch)