arXiv Machine Learning By James T. Pegg, Hubert Okadome Valencia, Ronin Wu

Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction

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arXiv:2607. 13737v1 Announce Type: new Abstract: For low-data and resource-constrained regimes typical of quantum chemistry, parameter-efficient learning is a key objective.

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arXiv Machine Learning
Sep 17

Active Learning Enables Generation of Molecules that Advance the Known Pareto Front

The paper presents a closed‑loop molecule generation pipeline that iteratively retrains on new quantum‑chemical simulation data, overcoming limitations of static generative models. This approach produces molecules whose properties extend up to 0.44 standard deviations beyond the training set and improves out‑of‑distribution classification accuracy by 79%. By conditioning on thermodynamic stability during the loop, the method yields a 3.5‑fold increase in the proportion of stable, potentially synthesizable molecules.

By Evan R. Antoniuk, Peggy Li, Nathan Keilbart, Stephen Weitzner, Bhavya Kailkhura, Anna M. Hiszpanski