COMBO: An efficient Bayesian optimization library for materials science

By Tsuyoshi Ueno, T. Rhone, Z. Hou, T. Mizoguchi, Koji Tsuda. 2016

312 citations recorded by the source corpus.

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ChemTS: an efficient python library for de novo molecular generation (2017, methodology): “The other is the combination of RNN and Bayesian optimization [21], where 4000 molecules are made a priori and Bayesian optimization is applied to find the best scoring molecule.”
A Tutorial on Bayesian Optimization (2018, background): “While there is some early work in these areas (Ueno et al., 2016; Frazier and Wang, 2016; Negoescu et al., 2011; Seko et al., 2015; Ju et al., 2017) the number of researchers working in these ﬁelds aware of the power and applicability of Bayesian optimization is still relatively small.”
Maximizing acquisition functions for Bayesian optimization (2018, background): “Recently, these strategies have demonstrated state-of-the-art results on many important, real-world problems ranging from material sciences [17, 57], to robotics [3, 7], to algorithm tuning and configuration [16, 29, 53, 56].”
Bayesian Optimization for Adaptive Experimental Design: A Review (2020, unknown): “neuroscience [47], [48], and materials discovery [49]–[55].”