Learning with Graph Kernels in the Chemical Universe

{"references": ["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"]}

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

Related Organizations

Lawrence Berkeley National Laboratory
United States

Keywords

machine learning, kernel, active learning, graph, molecular prediction, computational chemistry, similarity

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