most citedExplainability and Transferability of Machine Learning Models for Predicting the Glass Transition Temperature of Polymers

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

physics.chem-ph2026

Reaction-network reasoning with frontier models for experimentally confirmed catalyst-selectivity hypotheses

Sutanay Choudhury, Anwesha Banerjee, Udishnu Sanyal +7

Catalysts are essential for sustainable chemical manufacturing, yet discovering novel architectures remains a bottleneck dominated by trial-and-error experimentation and computatio…

cond-mat.soft2023★ 2 cited

Explainability and Transferability of Machine Learning Models for Predicting the Glass Transition Temperature of Polymers

Agrim Babbar, Sriram Ragunathan, Debirupa Mitra +2

Machine learning offers promising tools to develop surrogate models for polymer structure-property relations. Surrogate models can be built upon existing polymer data and are usefu…