Learning Continuous Solvent Effects from Transient Flow Data: A Graph Neural Network Benchmark on Catechol Rearrangement
Published:Dec 22, 2025 16:19
•1 min read
•ArXiv
Analysis
This article presents a benchmark for graph neural networks (GNNs) in the context of modeling solvent effects in chemical reactions, specifically focusing on the catechol rearrangement. The use of transient flow data suggests a focus on dynamic aspects of the reaction. The title clearly indicates the research area and the methodology employed.
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