Chemical Knowledge as Compression: Why Chemistry Still Needs Chemists in the Age of AI.

Authors

Oliver Steinbock, Wen Zhu, Wei Yang, Igor Alabugin

Published in

Chemistry (Weinheim an der Bergstrasse, Germany). Pages e71396. Jul 12, 2026. Epub Jul 12, 2026.

Abstract

Chemistry navigates an immense molecular space using compact, transferable knowledge structures that are neither strict physical laws nor merely qualitative intuition. We argue that this approach is best understood as compression: deliberate, lossy, and interpretable reductions of chemical details that generalize surprisingly well across molecules, reactions, and domains. Machine learning (ML) performs a different kind of compression, optimizing against training objectives rather than chemical judgment. The result is models that excel within their training domains but can fail outside them in ways that are difficult to diagnose, often without providing the mechanistic insight that makes chemical knowledge valuable and transferable. This difference defines an epistemic gap that is the central challenge for artificial intelligence (AI) in chemistry. Physics-based AI systems offer a partial path forward, but we believe this gap will persist for the foreseeable future with important consequences for how chemistry is practiced and taught.

PMID:
42437435
Bibliographic data and abstract were imported from PubMed on 12 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.