Unsupervised Thermodynamics of Molecular Diffusion Models: Action-Operator Semantics and Auditable Free-Energy Readout
arXiv:2606.30687v1 Announce Type: cross Abstract: Diffusion models are increasingly utilized for modeling molecular structures and conformational ensembles, yet the thermodynamic meaning of their learned representations and scores remains elusive. To resolve this ambiguity, we introduce a mathematically consistent action-operator framework natively compatible with diffusion models. By defining a fixed molecular environment as a base action $S_0(x)$ and an alchemical perturbation as an operator $O(x)$, standard diffusion noising induces effective noised actions and operators whose gradients and
The increasing utilization of diffusion models in molecular sciences necessitates a deeper understanding of their thermodynamic underpinnings, which this paper directly addresses.
Establishing a clear thermodynamic meaning for AI models in molecular design can unlock more reliable and interpretable applications in materials science and drug discovery.
This research provides a foundational framework to interpret and audit the thermodynamic implications of diffusion models, moving them from black-box tools to more transparent, designable systems.
- · AI-driven drug discovery
- · Materials science
- · Computational chemistry
- · AI interpretability research
- · Empirical molecular design without AI integration
Molecular diffusion models gain a rigorous thermodynamic interpretation, enhancing their reliability in scientific applications.
Improved understanding and control over these models could accelerate the discovery and optimization of new materials and pharmaceutical compounds.
The principle of 'action-operator semantics' could extend to other generative AI models, fostering a new era of explainable and thermodynamically consistent AI in scientific domains.
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Read at arXiv cs.AI