Teodoro Laino

Materials Chemistry · 2014–2020

A researcher in the Science corpus: 6 papers; trusts 23 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 85 citations into it; #7,268 of 11,513 by trust here, #5,715 by citations
• Computational Theory and Mathematics — 47 citations into it; #11,594 of 12,182 by trust here, #1,748 by citations
• Artificial Intelligence — 40 citations into it
• Computational Chemistry & Drug Discovery — 35 citations into it
• Molecular Biology — 28 citations into it
• Natural Language Processing — 21 citations into it
• Model Architectures — 20 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it

Whom Teodoro Laino trusts overall

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

1. Daniel M. Lowe
2. Connor W. Coley
3. Marwin H. S. Segler
4. Wengong Jin
5. Dzmitry Bahdanau
6. David Weininger
7. Yoshua Bengio
8. Alex Graves
9. Geoffrey E. Hinton
10. Diederik P. Kingma

Who trusts Teodoro Laino overall

1. Philippe Schwaller
2. José Jiménez-Luna
3. Seyone Chithrananda
4. Kevin Maik Jablonka

Papers from Teodoro Laino's perspective

Ordered by how strongly the researchers Teodoro Laino trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models (2017) — 325 citations
• Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction (2018) — 981 citations
• Solid-State Electrolytes: Revealing the Mechanisms of Li-Ion Conduction in Tetragonal and Cubic LLZO by First-Principles Calculations (2014) — 231 citations
• CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations. (2020) — 3,532 citations
• Mapping the space of chemical reactions using attention-based neural networks (2020) — 318 citations
• Unsupervised Attention-Guided Atom-Mapping (2020)