Guest Editorial: Special Topic on Data-enabled Theoretical Chemistry
Matthias Rupp, O. Anatole von Lilienfeld, Kieron Burke
TL;DR
This paper provides an overview of recent advances in data-enabled theoretical chemistry, highlighting key contributions and including a glossary of relevant machine learning concepts.
Contribution
It offers a comprehensive survey of the field, emphasizing the integration of data-driven methods into theoretical chemistry research.
Findings
Summarizes recent contributions in data-enabled theoretical chemistry
Provides a glossary of machine learning terms relevant to the field
Highlights the impact of data-driven approaches on chemical theory
Abstract
A survey of the contributions to the Special Topic on Data-enabled Theoretical Chemistry is given, including a glossary of relevant machine learning terms.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.