Benjamín Sánchez-Lengeling

Materials Chemistry · 2017–2021

A researcher in the Science corpus: 10 papers; trusts 61 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 161 citations into it; #1,057 of 11,513 by trust here, #2,695 by citations
• Computational Theory and Mathematics — 127 citations into it; #4,334 of 12,182 by trust here, #691 by citations
• Artificial Intelligence — 91 citations into it; #17,822 of 35,345 by trust here, #11,192 by citations
• Computational Chemistry & Drug Discovery — 76 citations into it; #153 of 425 by trust here, #182 by citations
• Molecular Biology — 69 citations into it
• Computer Vision and Pattern Recognition — 63 citations into it
• Generative Models — 58 citations into it; #583 of 1,956 by trust here, #501 by citations
• Graph Learning — 29 citations into it

Whom Benjamín Sánchez-Lengeling trusts overall

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

1. Sepp Hochreiter
2. Diederik P. Kingma
3. Ronald J. Williams
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Alex Graves
7. J. Urgen Schmidhuber
8. Alex Krizhevsky
9. Yann LeCun
10. Ilya Sutskever

Who trusts Benjamín Sánchez-Lengeling overall

1. Nicholas J. Schork
2. Dane Morgan
3. Tomohide Masuda
4. Rohit Batra
5. Zaccary Alperstein
6. Ryan Jacobs
7. Yongjun Hong
8. Mario Krenn
9. Alán Aspuru-Guzik
10. Matthew Ragoza

Papers from Benjamín Sánchez-Lengeling's perspective

Ordered by how strongly the researchers Benjamín Sánchez-Lengeling trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models (2017) — 580 citations
• Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models (2018) — 910 citations
• Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC) (2017) — 287 citations
• Inverse molecular design using machine learning: Generative models for matter engineering (2018) — 1,800 citations
• Inverse Design of Solid-State Materials via a Continuous Representation (2019) — 360 citations
• Rational design of layered oxide materials for sodium-ion batteries (2020) — 1,123 citations
• Design Principles and Top Non-Fullerene Acceptor Candidates for Organic Photovoltaics (2017) — 193 citations
• A Gentle Introduction to Graph Neural Networks (2021) — 294 citations
• Inverse design of nanoporous crystalline reticular materials with deep generative models (2020) — 348 citations
• Reinforced Adversarial Neural Computer for de Novo Molecular Design (2018) — 325 citations