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Transfer Learning for Molecular Property Predictions from Small Data Sets
April 23, 2024, 4:41 a.m. | Thorren Kirschbaum, Annika Bande
cs.LG updates on arXiv.org arxiv.org
Abstract: Machine learning has emerged as a new tool in chemistry to bypass expensive experiments or quantum-chemical calculations, for example, in high-throughput screening applications. However, many machine learning studies rely on small data sets, making it difficult to efficiently implement powerful deep learning architectures such as message passing neural networks. In this study, we benchmark common machine learning models for the prediction of molecular properties on small data sets, for which the best results are obtained …
abstract applications architectures arxiv chemistry cs.lg data data sets deep learning example however machine machine learning making physics.chem-ph predictions property quantum screening small small data studies tool transfer transfer learning type
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