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Learning with Graph Kernels in the Chemical Universe

Tang, Yu-Hang

Presentations slides of Yu-Hang Tang during the 2019 DSI Data Science Workshop on application of active machine learning and graph kernels. The talk also features the release of the GraphDot library.

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  • Tang, Y. H., & de Jong, W. A. (2019). Prediction of atomization energy using graph kernel and active learning. The Journal of chemical physics, 150(4), 044107. https://doi.org/10.1063/1.5078640

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Publication date:
August 8, 2019
DOI:
10.5281/zenodo.3364078

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Keyword(s):
machine learning graph active learning molecular prediction computational chemistry kernel similarity
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Creative Commons Attribution Non Commercial No Derivatives 4.0 International

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Version 3 10.5281/zenodo.3433276 Aug 8, 2019
Version 2 10.5281/zenodo.3401560 Aug 8, 2019
Version 1 10.5281/zenodo.3364078 Aug 8, 2019
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