AAAI 2025technical0 citations
De Novo Molecular and Crystal Design with Latent Space Bayesian Optimization
Abstract
This thesis explores Latent Space Bayesian Optimization (LSBO) for the generation and optimization of de novo molecules and crystal materials. Our goal is to develop practical, sample-efficient de novo discovery algorithms with a focus on real-world applicability, and our results so far demonstrate significant progress toward practical implementation.
BibTeX
@article{Boyar_2025, title={De Novo Molecular and Crystal Design with Latent Space Bayesian Optimization}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35201}, DOI={10.1609/aaai.v39i28.35201}, abstractNote={This thesis explores Latent Space Bayesian Optimization (LSBO) for the generation and optimization of de novo molecules and crystal materials. Our goal is to develop practical, sample-efficient de novo discovery algorithms with a focus on real-world applicability, and our results so far demonstrate significant progress toward practical implementation.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Boyar, Onur}, year={2025}, month={Apr.}, pages={29247-29248} }