The central role of density functional theory in the AI age
- Author(s)
- Bing Huang, Guido Falk von Rudorff, O. Anatole von Lilienfeld
- Abstract
Density functional theory (DFT) plays a pivotal role in chemical and materials science because of its relatively high predictive power, applicability, versatility, and computational efficiency. We review recent progress in machine learning (ML) model developments, which have relied heavily on DFT for synthetic data generation and for the design of model architectures. The general relevance of these developments is placed in a broader context for chemical and materials sciences. DFT-based ML models have reached high efficiency, accuracy, scalability, and transferability and pave the way to the routine use of successful experimental planning software within self-driving laboratories.
- Organisation(s)
- Computational Materials Physics
- External organisation(s)
- Universität Kassel, Vector Institute for Artificial Intelligence, University of Toronto, Technische Universität Berlin
- Journal
- Science
- Volume
- 381
- Pages
- 170-175
- No. of pages
- 6
- ISSN
- 0036-8075
- DOI
- https://doi.org/10.1126/science.abn3445
- Publication date
- 07-2023
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103006 Chemical physics, 102019 Machine learning
- ASJC Scopus subject areas
- General
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/the-central-role-of-density-functional-theory-in-the-ai-age(197ca089-8a78-4bd6-9478-0fc307153f5b).html