What is it about?
This paper explores the development of a more accurate method for predicting whether molecules will remain stable or dissociate, without relying on expensive experiments or simulations. Instead of using a single number to describe stability, we employ a physics-informed description that captures the interactions between molecules. This helps make predictions that are applicable across various chemical systems, not just a single specific case.
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Why is it important?
Knowing whether molecules are stable is crucial in areas such as drug discovery, materials design, and chemical manufacturing. Many existing prediction methods are limited to specific datasets and often fail when applied to new systems. By focusing on the physical nature of molecular interactions, this work improves reliability and generalisation. This can reduce trial-and-error, save time and resources, and help scientists make more informed decisions earlier in the research process.
Perspectives
This work arose from my curiosity about why many predictive models perform well on paper but fail when applied to new chemical systems. While working independently, I realised that supramolecular stability cannot be reduced to a single energy value. This paper reflects my attempt to reintroduce physical intuition into prediction, marking the starting point of a broader effort to build models that generalise because they respect underlying chemistry, not just data.
Prasanna Kulkarni
Institute of Chemical Technology
Read the Original
This page is a summary of: A Physics-Informed Fingerprint for Generalizable Prediction of Supramolecular Stability, The Journal of Physical Chemistry, December 2025, American Chemical Society (ACS),
DOI: 10.1021/acs.jpcb.5c07262.
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