José Jiménez-Luna

Materials Chemistry · 2020–2025

A researcher in the Science corpus: 5 papers; trusts 95 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 130 citations into it; #3,975 of 11,513 by trust here, #8,063 by citations
• Computational Theory and Mathematics — 125 citations into it; #5,937 of 12,182 by trust here, #3,562 by citations
• Molecular Biology — 121 citations into it
• Artificial Intelligence — 103 citations into it; #19,957 of 35,345 by trust here, #21,316 by citations
• Computational Chemistry & Drug Discovery — 53 citations into it
• Safety, Robustness & Interpretability — 40 citations into it
• Computer Vision and Pattern Recognition — 32 citations into it
• Spectroscopy — 27 citations into it

Whom José Jiménez-Luna trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. J. Urgen Schmidhuber
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Zoubin Ghahramani
8. Yann LeCun
9. Ilya Sutskever
10. Connor W. Coley

Who trusts José Jiménez-Luna overall

1. Kenneth Atz
2. Francesca Grisoni
3. Michelle M. Li
4. Gisbert Schneider
5. Kexin Huang
6. Anastasiia V. Sadybekov

Papers from José Jiménez-Luna's perspective

Ordered by how strongly the researchers José Jiménez-Luna 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. 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. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Drug discovery with explainable artificial intelligence (2020) — 857 citations
• Artificial intelligence in drug discovery: recent advances and future perspectives (2021) — 319 citations
• QMugs, quantum mechanical properties of drug-like molecules (2022) — 164 citations
• Δ-Quantum machine-learning for medicinal chemistry (2021) — 37 citations
• Scalable emulation of protein equilibrium ensembles with generative deep learning (2025) — 335 citations