Stefan Chmiela
Materials Chemistry · 2016–2021
A researcher in the Science corpus: 8 papers; trusts 40 researchers and is trusted by 73 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 393 citations into it; #220 of 11,513 by trust here, #1,101 by citations
- Atomic and Molecular Physics, and Optics — 219 citations into it; #3,112 of 7,135 by trust here, #2,433 by citations
- Computational Theory and Mathematics — 199 citations into it; #1,616 of 12,182 by trust here, #156 by citations
- Molecular Biology — 156 citations into it; #1,705 of 17,773 by trust here, #1,622 by citations
- Computational Chemistry & Drug Discovery — 146 citations into it; #37 of 425 by trust here, #32 by citations
- Artificial Intelligence — 126 citations into it; #17,539 of 35,345 by trust here, #7,112 by citations
- Physical and Theoretical Chemistry — 98 citations into it; #206 of 671 by trust here, #56 by citations
- Computer Vision and Pattern Recognition — 62 citations into it
Whom Stefan Chmiela trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alexandre Tkatchenko
- A. P. Bart'ok
- J. Behler
- Kristof T. Schütt
- Geoffrey E. Hinton
- Mark E. Tuckerman
- K. Müller
- Yann LeCun
- Yoshua Bengio
- Diederik P. Kingma
Who trusts Stefan Chmiela overall
- Johannes Klicpera
- Wojciech Samek
- Risi Kondor
- Chi Chen
- K. Müller
- Kristof T. Schütt
- Oliver T. Unke
- Albert Musaelian
- Grgoire Montavon
- Simon L. Batzner
Papers from Stefan Chmiela's perspective
Ordered by how strongly the researchers Stefan Chmiela trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- SchNet: A continuous-filter convolutional neural network for modeling quantum interactions (2017) — 1,449 citations
- Quantum-chemical insights from deep tensor neural networks (2016) — 1,416 citations
- Machine learning of accurate energy-conserving molecular force fields (2016) — 1,263 citations
- Towards exact molecular dynamics simulations with machine-learned force fields (2018) — 743 citations
- Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems (2021) — 675 citations
- Machine Learning Force Fields (2020) — 1,403 citations
- Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach. (2020) — 49 citations
- SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects (2021) — 371 citations