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Draft:VeloxChem

From Wikipedia, the free encyclopedia
  • Comment: This is almost completely sourced to primary papers from one lab (judging by overlap in co-authors). The eChem book overlaps in authorship too. Wikipedia needs substantial coverage by independent sources, and I'm just not seeing it here. It sounds like an excellent piece of software, but Wikipedia notability requires external recognition which is quite difficult for software products. You might find the advice (it is not a policy) at Wikipedia:Notability_(software) helpful. M kuhner (talk) 23:12, 7 August 2026 (UTC)

Computational chemistry and in particular quantum chemistry has reached a level of accuracy such that it has become an indispensable tool for scientific discovery in (bio-)chemical and materials sciences. One can identify three reasons for this success namely the development of reduced-scaling methods, the development of stable and efficient numerical algorithms combined with their implementation in available software programs, and the development of hardware that is highly performant for floating-point operations.

VeloxChem is one such quantum chemistry program.[1] that position itself as a provider of highly efficient implementations of first principles electronic structure theory on modern hardware with GPU accelerations. The name is derived from the Latin word velox, meaning "swift" or "rapid". It is a free, open-source software released under the BSD 3-clause license. Its main developer node is the KTH Center for Scientific Computing (KCSC) at the KTH Royal Institute of Technology (KTH) in Stockholm, Sweden.

History

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The VeloxChem project was started in 2018 by a core team of researchers in the Division of Theoretical Chemistry and Biology at KTH with the aim to create a science- and education-enabling software, allowing for efficient massively parallel execution in high-performance computing (HPC) environments. It adopted an object-oriented software engineering approach with numerical solvers and chemistry methods implemented in the Python programming language, while compute intensive routines are implemented in C++/CUDA/HIP [2].

Features

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VeloxChem presents a means for interactive training and education [3] as well as accelerated method development [4] through Jupyter notebooks. Modeling of complex molecular systems is facilitated by a strong focus on quantum-classical interoperability and semi-automatized workflows [5]. Solvents and environments are modeled implicitly and explicitly by means of the conductor‑like polarizable continuum model (CPCM) and the polarizable embedding (PE) model, respectively.

A selection of electronic structure theory methods are made available by means of seamless module integration. The core VeloxChem module implements the Hartree–Fock method (HF) and Kohn–Sham density functional theory (DFT). Modules offering post-HF methods are developed in an international network collaboration and include:

  • MultiPsi [6] for multi-configuration self-consistent field (MCSCF) and multi-configurational pair-density functional (MC-PDFT) theory
  • adcc [7] for algebraic diagrammatic construction theory
  • Penguin [8] for coupled cluster theory

VeloxChem implements real and complex (damped) response theory, also known as the time-dependent DFT and complex polarization propagator (CPP) approaches, respectively [9]. Linear, quadratic, and cubic response functions are made available for all rungs of Jacob's ladder of exchange-correlation functionals [10], which facilitates modeling of single- and multi-photon spectroscopies [11]

VeloxChem demonstrates exceptional performance on HPC cluster resources with GPU-accelerated nodes, enabling applications with large-scale molecular systems and varying spectroscopies such as infra-red absorption [12] and electronic circular dichroism [13]. Notably, in a collaboration between KTH and CSC – IT Center for Science and AMD Silo AI, VeloxChem has demonstrated to scale DFT-based linear response calculations up to the size of the entire LUMI-G supercomputer with employment of some 22,000 GPU devices.

See also

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  1. ↑ Crawford, T. Daniel, and Andreas Dreuw. “Quantum Chemistry Software for Molecules and Materials. J. Phys. Chem. A 2026, 130 (14), 2785–2787. https://doi.org/10.1021/acs.jpca.6c01501
  2. ↑ Rinkevicius, Z.; Li, X.; Vahtras, O.; Ahmadzadeh, K.; Brand, M.; Ringholm, M.; List, N. H.; Scheurer, M.; Scott, M.; Dreuw, A.; Norman, P. VeloxChem: A Python‐driven Density‐functional Theory Program for Spectroscopy Simulations in High‐performance Computing Environments. WIREs Comput Mol Sci 2020, 10 (5), e1457. https://doi.org/10.1002/wcms.1457
  3. ↑ Fransson, T.; Delcey, M. G.; Brumboiu, I. E.; Hodecker, M.; Li, X.; Rinkevicius, Z.; Dreuw, A.; Rhee, Y. M.; Norman, P. eChem: A Notebook Exploration of Quantum Chemistry. J. Chem. Educ. 2023, 100 (4), 1664–1671. https://doi.org/10.1021/acs.jchemed.2c01103
  4. ↑ Hodecker, M.; Norman, P.; Brumboiu, I. E. eChem: Accelerated Method Development in Quantum Chemistry with Notebooks. Chem. Methods 2025, 2500033. https://doi.org/10.1002/cmtd.202500033
  5. ↑ De Gracia Trivino, J. A.; Brumboiu, I. E.; Carrasco-Busturia, D.; Li, X.; Li, C.; Linares, M.; Lindfeld, V.; Rhee, Y. M.; Rune, J.; Van Hoorn, B.; Norman, P.; Ahlquist, M. S. G. VeloxChem Quantum–Classical Interoperability for Modeling of Complex Molecular Systems. J. Phys. Chem. A 2025, 129 (32), 7575–7587. https://doi.org/10.1021/acs.jpca.5c03187
  6. ↑ Delcey, M. G. MultiPsi : A Python‐driven MCSCF Program for Photochemistry and Spectroscopy Simulations on Modern HPC Environments. WIREs Comput. Mol. Sci. 2023 13 (6), e1675. https://doi.org/10.1002/wcms.1675
  7. ↑ Herbst, M. F.; Scheurer, M.; Fransson, T.; Rehn, D. R.; Dreuw, A. Adcc: A Versatile Toolkit for Rapid Development of Algebraic‐diagrammatic Construction Methods. WIREs Comput. Mol. Sci. 2020, 10 (6), e1462. https://doi.org/10.1002/wcms.1462.
  8. ↑ Hillers-Bendtsen, A. E.; Johansen, M. B.; Von Buchwald, T. J.; Dünweber, P. G. I. L.; Olsen, L. H.; Rask, L.; Junker, G. I.; Knudsen, R. M. H.; Mikkelsen, K. V. Penguin: A Python-Based Program for Electronic Structure Calculations Based on Coupled Cluster Theory. J. Phys. Chem. A 2025, 129 (45), 10571–10582. https://doi.org/10.1021/acs.jpca.5c02513
  9. ↑ Norman, P.; Ruud, K.; Saue, T. Principles and Practices of Molecular Properties: Theory, Modeling and Simulations, First edition.; John Wiley & Sons: Hoboken, NJ, 2018
  10. ↑ Ahmadzadeh, K.; Li, X.; Rinkevicius, Z.; Norman, P.; Zaleśny, R. Toward Accurate Two-Photon Absorption Spectrum Simulations: Exploring the Landscape beyond the Generalized Gradient Approximation. J. Phys. Chem. Lett. 2024, 15 (4), 969–974. https://doi.org/10.1021/acs.jpclett.3c03513
  11. ↑ Ahmadzadeh, K.; Zaleśny, R.; Li, X.; Rinkevicius, Z.; Hu, W.; Norman, P. Effects of Molecular Aggregation on Dynamic Third-Order Nonlinear Optical Responses: Oligo(Thiophene-Benzothiadiazole) as a Case Study. J. Chem. Theory Comput. 2026, acs.jctc.6c00268. https://doi.org/10.1021/acs.jctc.6c00268
  12. ↑ Andersen, J. H.; Brumboiu, I. E.; Hodecker, M.; Li, X.; Norman, P.; Rinkevicius, Z. VeloxChem: Large-Scale DFT Calculations of Geometric Derivatives up to Second Order for Simulation of IR Spectra J. Phys. Chem. A 2026, 130 (2), 569–580. https://doi.org/10.1021/acs.jpca.5c04510
  13. ↑ Li, X.; Linares, M.; Norman, P. VeloxChem: GPU-Accelerated Fock Matrix Construction Enabling Complex Polarization Propagator Simulations of Circular Dichroism Spectra of G-Quadruplexes. J. Phys. Chem. A 2025, 129 (2), 633–642. https://doi.org/10.1021/acs.jpca.4c07510
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