Oliver T. Unke

Materials Chemistry · 2017–2022

A researcher in the Science corpus: 8 papers; trusts 61 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 276 citations into it; #787 of 11,513 by trust here, #3,987 by citations
• Atomic and Molecular Physics, and Optics — 178 citations into it; #7,071 of 7,135 by trust here, #4,097 by citations
• Computational Theory and Mathematics — 145 citations into it; #1,383 of 12,182 by trust here, #836 by citations
• Molecular Biology — 120 citations into it; #1,740 of 17,773 by trust here, #5,084 by citations
• Computational Chemistry & Drug Discovery — 117 citations into it; #142 of 425 by trust here, #231 by citations
• Artificial Intelligence — 109 citations into it
• Physical and Theoretical Chemistry — 82 citations into it
• Model Architectures — 66 citations into it; #1,003 of 2,278 by trust here, #1,246 by citations

Whom Oliver T. Unke trusts overall

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

1. J. Behler
2. Geoffrey E. Hinton
3. Kristof T. Schütt
4. Alexandre Tkatchenko
5. Kaiming He
6. Yoshua Bengio
7. Simon S. Haykin
8. Matthias Rupp 
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Oliver T. Unke overall

1. Kenneth Atz
2. Simon L. Batzner
3. Johannes Klicpera
4. Albert Musaelian
5. Pascal Friederich
6. Francesca Grisoni
7. Michael Gastegger
8. Gisbert Schneider
9. Shuo Zhang
10. Tess E. Smidt

Papers from Oliver T. Unke's perspective

Ordered by how strongly the researchers Oliver T. Unke 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Equivariant message passing for the prediction of tensorial properties and molecular spectra (2021) — 803 citations
• PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges. (2019) — 1,056 citations
• Machine Learning Force Fields (2020) — 1,403 citations
• SE(3)-equivariant prediction of molecular wavefunctions and electronic densities (2021) — 127 citations
• Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations (2022) — 20 citations
• SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects (2021) — 371 citations
• A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information. (2018) — 88 citations
• Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces (2017) — 121 citations