The Amsterdam Modeling Suite
In this paper, we present the Amsterdam Modeling Suite (AMS), a comprehensive software platform designed to support advanced molecular and materials simulations across a wide range of chemical and physical systems. AMS integrates cutting-edge quantum chemical methods, including Density Functional Theory (DFT) and time-dependent DFT, with molecular mechanics, fluid thermodynamics, machine learning techniques, and more, to enable multi-scale modeling of complex chemical systems. Its design philosophy allows for seamless coupling between components, facilitating simulations that range from small molecules to complex biomolecular and solid-state systems, making it a versatile tool for tackling interdisciplinary challenges, both in industry and in academia. The suite also emphasizes user accessibility, with an intuitive graphical interface, extensive scripting capabilities, and compatibility with high-performance computing environments.
Authors
- Stan van Gisbergen
- J. M. Thijssen (ORCID: https://orcid.org/0000-0003-2172-1555)
- Jochen Autschbach (ORCID: https://orcid.org/0000-0001-9392-877X)
- Thomas Heine (ORCID: https://orcid.org/0000-0003-2379-6251)
- Tomáš Trnka (ORCID: https://orcid.org/0000-0002-9273-3820)
- Mariusz P. Mitoraj (ORCID: https://orcid.org/0000-0001-5359-9107)
- Néstor F. Aguirre (ORCID: https://orcid.org/0000-0001-7901-0479)
- Valentin Paul Nicu
- F. Matthias Bickelhaupt (ORCID: https://orcid.org/0000-0003-4655-7747)
- Artur Michalak (ORCID: https://orcid.org/0000-0003-1408-5474)
- Christoph R. Jacob (ORCID: https://orcid.org/0000-0002-6227-8476)
- A. Förster (ORCID: https://orcid.org/0000-0002-0957-4081)
- Evert Jan Baerends (ORCID: https://orcid.org/0000-0002-3045-4906)
- Célia Fonseca Guerra (ORCID: https://orcid.org/0000-0002-2973-5321)
- T. P. M. Goumans (ORCID: https://orcid.org/0009-0001-6543-3195)
- Alexei Yakovlev
- Harry Ramanantoanina (ORCID: https://orcid.org/0000-0002-2086-6958)
- Rosa E. Bulo (ORCID: https://orcid.org/0000-0002-5325-5731)
- Robert Rüger (ORCID: https://orcid.org/0000-0001-6907-5084)
- Erik van Lenthe
- Lucas Visscher (ORCID: https://orcid.org/0000-0002-7748-6243)
- Marcel Swart (ORCID: https://orcid.org/0000-0002-8174-8488)
- Franco Egidi (ORCID: https://orcid.org/0000-0003-3259-8863)
- Lasse Jensen (ORCID: https://orcid.org/0000-0003-1237-5021)
- Mykhaylo Krykunov
- Johannes Neugebauer (ORCID: https://orcid.org/0000-0002-8923-4684)
- Pier Philipsen (ORCID: https://orcid.org/0000-0003-0280-9607)
- N. Austin (ORCID: https://orcid.org/0000-0002-2795-8150)
- Georg Schreckenbach (ORCID: https://orcid.org/0000-0002-4614-0901)
- Adri C. T. van Duin (ORCID: https://orcid.org/0000-0002-3478-4945)
- Mirko Franchini
- Chiara Cappelli (ORCID: https://orcid.org/0000-0002-4872-4505)
- Matti Hellström (ORCID: https://orcid.org/0000-0003-3053-5658)
- Mauro Stener (ORCID: https://orcid.org/0000-0003-3700-7903)
Institutions
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Jagiellonian University (PL)
- IMT School for Advanced Studies Lucca (IT)
- Pennsylvania State University (US)
- University of Trieste (IT)
- Radboud University Nijmegen (NL)
- Scuola Normale Superiore (IT)
- Johannes Gutenberg University Mainz (DE)
- Masaryk University (CZ)
- University of Münster (DE)
- University of Johannesburg (ZA)
- Universitat de Girona (ES)
- Scientific Computing & Modelling (Netherlands) (NL)
- University of Manitoba (CA)
- University at Buffalo, State University of New York (US)
- Technische Universität Dresden (DE)
- Vrije Universiteit Amsterdam (NL)
- Technische Universität Braunschweig (DE)
- Delft University of Technology (NL)
Publication Details
- Journal
- The Journal of Chemical Physics
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1063/5.0258496
- Citations
- 80
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 40.91