ST John

Materials Chemistry · 2016

A researcher in the Science corpus: 1 paper; trusts 27 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 24 citations into it; #5,039 of 11,513 by trust here, #10,400 by citations
• Molecular Biology — 23 citations into it; #8,249 of 17,773 by trust here, #15,203 by citations
• Atomic and Molecular Physics, and Optics — 17 citations into it
• Statistical and Nonlinear Physics — 8 citations into it
• Physical and Theoretical Chemistry — 7 citations into it
• Computational Chemistry & Drug Discovery — 6 citations into it
• Computational Theory and Mathematics — 6 citations into it; #7,138 of 12,182 by trust here, #7,851 by citations
• Condensed Matter Physics — 6 citations into it

Whom ST John trusts overall

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

1. J. Kästner
2. A. P. Bart'ok
3. S. Plimpton
4. Alessandro Laio
5. Edward Snelson
6. Michael W. Mahoney
7. Eric F Darve
8. W. Noid
9. Matthias W. Seeger
10. T. Darden

Who trusts ST John overall

1. Frank No'e
2. Brooke E. Husic
3. Alexandre Tkatchenko
4. K. Müller
5. Jiang Wang
6. Cecilia Clementi
7. Nicholas E. Charron

Papers from ST John's perspective

Ordered by how strongly the researchers ST John 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Learning Multiple Layers of Features from Tiny Images
5. Information Theory, Inference, and Learning Algorithms
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials. (2016) — 126 citations