VeloxChem Quantum–Classical Interoperability for Modeling of Complex Molecular Systems
Being a program written primarily in Python that strictly adheres to modern object-oriented software engineering and parallel programming practices, VeloxChem is shown to be suitable for the development of (semi)automatized workflows that extend its scope from first-principles quantum chemical purism to hybrid quantum-classical interoperability and some degree of semiempiricism. Methods are presented for building complex systems such as metal-organic frameworks, constructing molecular mechanics and interpolation mechanics force fields, conformer searches, system solvation, determining free energies of solvation, and determining free energy profiles of reaction pathways using the empirical valence bond method. The implementations are made intuitive with opportunities for interactive plotting and 3D molecular structure illustrations through the use of Jupyter notebooks.
Authors
- Young Min Rhee (ORCID: https://orcid.org/0000-0002-2392-3962)
- Mathieu Linares (ORCID: https://orcid.org/0000-0002-9720-5429)
- Mårten S. G. Ahlquist (ORCID: https://orcid.org/0000-0002-1553-4027)
- Patrick Norman (ORCID: https://orcid.org/0000-0002-1191-4954)
- Juan Ángel de Gracia Triviño (ORCID: https://orcid.org/0000-0002-7563-8944)
- Iulia Emilia Brumboiu (ORCID: https://orcid.org/0000-0003-1671-8298)
- David Carrasco‐Busturia (ORCID: https://orcid.org/0000-0003-1588-338X)
- Julia Rune
- Xin Li (ORCID: https://orcid.org/0000-0001-6508-8355)
- Chenxi Li (ORCID: https://orcid.org/0000-0003-0129-1957)
- Bastiaan van Hoorn
- Valentin Lindfeld
Institutions
- Korea Advanced Institute of Science and Technology (KR)
- Center for Theoretical Physics (PL)
- Nicolaus Copernicus University (PL)
- National Center for High-Performance Computing (TW)
- Centre for High Performance Computing (ZA)
- KTH Royal Institute of Technology (SE)
Publication Details
- Journal
- The Journal of Physical Chemistry A
- Published
- 2025-08-03
- DOI
- https://doi.org/10.1021/acs.jpca.5c03187
- Citations
- 2
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- Swedish e-Science Research Centre
- Vetenskapsrådet