Rafael Gómez‐Bombarelli

Computational Theory and Mathematics · 2015–2020

A researcher in the Science corpus: 8 papers; trusts 34 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 103 citations into it; #573 of 12,182 by trust here, #38 by citations
• Materials Chemistry — 102 citations into it; #311 of 11,513 by trust here, #650 by citations
• Computational Chemistry & Drug Discovery — 67 citations into it; #171 of 425 by trust here, #47 by citations
• Molecular Biology — 61 citations into it; #2,205 of 17,773 by trust here, #1,166 by citations
• Artificial Intelligence — 36 citations into it
• Atomic and Molecular Physics, and Optics — 24 citations into it
• Model Architectures — 21 citations into it
• Spectroscopy — 20 citations into it

Whom Rafael Gómez‐Bombarelli trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. David Weininger
5. Yann LeCun
6. Stefan Grimme
7. Alex Krizhevsky
8. Noel Cressie
9. Alex Graves
10. Jasper Snoek

Who trusts Rafael Gómez‐Bombarelli overall

1. Wengong Jin
2. José Miguel Hernández-Lobato
3. Shitong Luo
4. Ryan-Rhys Griffiths
5. Minkai Xu
6. Chence Shi
7. Regina Barzilay
8. Tommi S. Jaakkola
9. Yuanqi Du
10. Erik A. Daxberger

Papers from Rafael Gómez‐Bombarelli's perspective

Ordered by how strongly the researchers Rafael Gómez‐Bombarelli trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Convolutional Networks on Graphs for Learning Molecular Fingerprints (2015) — 3,666 citations
• Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules (2016) — 3,556 citations
• GEOM: Energy-annotated molecular conformations for property prediction and molecular generation (2020) — 347 citations
• Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. (2016) — 926 citations
• What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery (2015) — 286 citations
• Design of e cient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach (2016)
• Anthraquinone Derivatives in Aqueous Flow Batteries (2016) — 216 citations
• A redox-flow battery with an alloxazine-based organic electrolyte (2016) — 461 citations