Shashanka Ubaru, Sanjeeb Dash, et al.
NeurIPS 2020
Computer-aided synthesis design, automation, and analytics assisted by machine learning are promising resources in the researcher’s toolkit. Each component may alleviate the chemist from routine tasks, provide valuable insights from data, and enable more informed experimental design. Herein, we highlight selected works in the field and discuss the different approaches and the problems to which they may apply. We emphasize that there are currently few tools with a low barrier of entry for non-experts, which may limit widespread integration into the researcher’s workflow.
Shashanka Ubaru, Sanjeeb Dash, et al.
NeurIPS 2020
Pengfei He, Han Xu, et al.
ICLR 2024
Oliver Bodemer
IBM J. Res. Dev
SUBHAJIT CHAUDHURY, Toshihiko Yamasaki
ICASSP 2024