In chemistry, models such as simplified molecular-input line-entry system (SMILES)-VAE155 can transform SMILES strings, which are molecular notations of chemical structures in the form of a discrete series of symbols that computers can easily understand, into a differentiable latent space that can be optimized using Bayesian optimization techniques (Fig. 3c).
This could be useful for chemistry research for robotic labs.
[Nam, NeurIPS, 2022]. "Reinforcement Learning with State ObservationCosts in Action-Contingent Noiselessly Observable Markov Decision Processes"
Discussion on
Bellinger C, Drozdyuk A, Crowley M, Tamblyn I. Balancing Information with Observation Costs in Deep Reinforcement Learning. Proceedings of the Canadian Conference on Artificial Intelligence [Internet]. 2022 May 27; Available from: https://caiac.pubpub.org/pub/0jmy7gpd