Chemical Knowledge as Compression: Why Chemistry Still Needs Chemists in the Age of AI.
TL;DR
It is argued 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.
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.