Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach.
By Rafael Gómez‐Bombarelli, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, David Kristjanson Duvenaud, Dougal Maclaurin, M. Blood-Forsythe, H. Chae, Markus Einzinger, Dong-Gwang Ha, Tony C. Wu and 11 more. 2016
926 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Materials Chemistry, Computational Chemistry & Drug Discovery, AI for Physical Sciences
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
- S. Reineke
- Hajime Nakanotani
- Aron Walsh
- Keith T. Butler
- Ryan-Rhys Griffiths
- José Miguel Hernández-Lobato
- Ryan P. Adams
- Matt J. Kusner
In Materials Chemistry
- José Miguel Hernández-Lobato
- Ryan-Rhys Griffiths
- Jos van der Westhuizen
- David Janz
- Hajime Nakanotani
- S. Reineke
- Keith T. Butler
- Aron Walsh
In Computational Chemistry & Drug Discovery
- José Miguel Hernández-Lobato
- Ryan-Rhys Griffiths
- Philippe Schwaller
- Aron Walsh
- Ryan P. Adams
- Matt J. Kusner
- Olexandr Isayev
- Hugh M. Cartwright
In AI for Physical Sciences
Citations in the corpus
- Grammar Variational Autoencoder (2017, methodology): “Given this we perform optimization in this latent space using Bayesian optimization, inspired by the technique of Gómez-Bombarelli et al. (2016b).”
- Constrained Bayesian Optimization for Automatic Chemical Design (2017, methodology): “This approach is followed for the SMILES format both by Gómez-Bombarelli et al. [2018] and here.”
- Actively Learning what makes a Discrete Sequence Valid (2017, background): “G´omez-Bombarelli et al. (2016b) showed that is possible to use this technique to search for promising drug molecules.”