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cs.LG2025
Active Learning Enables Generation of Molecules that Advance the Known Pareto Front
Evan R. Antoniuk, Peggy Li, Nathan Keilbart +3
Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the proper…
cs.LG2021★ 5 cited
Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy
Tim Hsu, Tuan Anh Pham, Nathan Keilbart +6
Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does n…