Evolutionary machine learning of physics-based force fields in high-dimensional parameter-space
This work presents the Alexandria Chemistry Toolkit (ACT), an open-source software for machine learning of physics-based force fields (FFs) from scratch, based on user-specified potential functions.
Authors
- David van der Spoel (ORCID: https://orcid.org/0000-0002-7659-8526)
- Marie‐Madeleine Walz (ORCID: https://orcid.org/0000-0002-3932-5780)
- A. Hosseini (ORCID: https://orcid.org/0000-0002-9610-5151)
- Kristian Kříž (ORCID: https://orcid.org/0000-0001-9072-0949)
- Paul J. van Maaren (ORCID: https://orcid.org/0000-0002-6068-4585)
- J. Martins (ORCID: https://orcid.org/0000-0002-2120-2782)
- Alfred Nordman
- Julián Ramón Marrades Furquet
- Mohammad Mehdi Ghahremnapour
Institutions
- Universidade Federal de Minas Gerais (BR)
- Uppsala University (SE)
- Yale University (US)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5dd00178a
- Citations
- 5
- Primary Topic
- Evolutionary Algorithms and Applications
- Type
- article
- Field-Weighted Citation Impact
- 12.40
Funders
- Kungliga Tekniska Högskolan
- Vetenskapsrådet
- China Scholarship Council
- Uppsala Universitet