Philippe Schwaller

Materials Chemistry · 2016–2025

A researcher in the Science corpus: 9 papers; trusts 35 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 140 citations into it; #9,338 of 11,513 by trust here, #3,935 by citations
• Computational Theory and Mathematics — 117 citations into it
• Artificial Intelligence — 114 citations into it
• Molecular Biology — 92 citations into it
• Computational Chemistry & Drug Discovery — 63 citations into it
• Natural Language Processing — 38 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Evaluation & Benchmarks — 32 citations into it

Whom Philippe Schwaller 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. David Weininger
6. Alex Graves
7. Ronald J. Williams
8. Yoshua Bengio
9. J. Urgen Schmidhuber
10. Regina Barzilay

Who trusts Philippe Schwaller overall

1. Andrés M Bran
2. José Jiménez-Luna
3. Francesca Grisoni
4. Philipp Seidl
5. Kevin Maik Jablonka
6. Seyone Chithrananda
7. Ross Irwin
8. Tianyang Lin
9. Carl N. Edwards
10. Kenneth Atz

Papers from Philippe Schwaller's perspective

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

Their papers

• A benchmark of expert-level academic questions to assess AI capabilities (2025) — 349 citations
• Augmenting large language models with chemistry tools (2023) — 1,063 citations
• “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models (2017) — 325 citations
• Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds (2016) — 1,604 citations
• Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction (2018) — 981 citations
• Mapping the space of chemical reactions using attention-based neural networks (2020) — 318 citations
• Leveraging large language models for predictive chemistry (2024) — 403 citations
• Artificial Intelligence For Natural Product Drug Discovery (2023) — 341 citations
• Unsupervised Attention-Guided Atom-Mapping (2020)