Huziel E. Sauceda

Materials Chemistry · 2016–2021

A researcher in the Science corpus: 5 papers; trusts 19 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 216 citations into it; #631 of 11,513 by trust here, #2,144 by citations
• Atomic and Molecular Physics, and Optics — 130 citations into it; #3,111 of 7,135 by trust here, #2,433 by citations
• Computational Theory and Mathematics — 107 citations into it; #2,153 of 12,182 by trust here, #334 by citations
• Computational Chemistry & Drug Discovery — 83 citations into it; #46 of 425 by trust here, #59 by citations
• Artificial Intelligence — 68 citations into it
• Molecular Biology — 68 citations into it; #2,225 of 17,773 by trust here, #2,641 by citations
• Physical and Theoretical Chemistry — 60 citations into it
• AI for Physical Sciences — 35 citations into it; #68 of 247 by trust here, #28 by citations

Whom Huziel E. Sauceda trusts overall

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

1. J. Behler
2. Alexandre Tkatchenko
3. A. P. Bart'ok
4. Mark E. Tuckerman
5. Kristof T. Schütt
6. Geoffrey E. Hinton
7. Grgoire Montavon
8. Matthias Rupp 
9. Yann LeCun
10. Yoshua Bengio

Who trusts Huziel E. Sauceda overall

1. Johannes Klicpera
2. Oliver T. Unke
3. Rohit Batra
4. Chi Chen
5. Albert Musaelian
6. Kristof T. Schütt
7. Wojciech Samek
8. Simon L. Batzner
9. G. P. P. Pun
10. Pascal Friederich

Papers from Huziel E. Sauceda's perspective

Ordered by how strongly the researchers Huziel E. Sauceda 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. Attention is All you Need (Neural Information Processing Systems)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Machine learning of accurate energy-conserving molecular force fields (2016) — 1,263 citations
• SchNet - A deep learning architecture for molecules and materials. (2017) — 2,154 citations
• Towards exact molecular dynamics simulations with machine-learned force fields (2018) — 743 citations
• Machine Learning Force Fields (2020) — 1,403 citations
• SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects (2021) — 371 citations