Design of e cient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules (2016, methodology): “Current methods either exhaustively search through a ﬁxed library, [8,9] or use discrete local search methods such as genetic algorithms [10–15] or similar discrete interpolation techniques.”
Constrained Bayesian optimization for automatic chemical design using variational autoencoders (2019, unknown): “The use of geometrical cues such as gradients to guide the search in continuous space in conjunction with advances in Bayesian optimization methodologies 16,17 could accelerate both drug 14,18 and materials 19,20 discovery by functioning as a high-throughput virtual screen of unpromising candidates.”
Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC) (2017, background): “This strategy has achieved many important triumphs in pharmaceutical contexts [6, 7], design of organic photovoltaics [8, 9], OLEDs [10] and ﬂow batteries [11, 12], among many others.”