Michael Gastegger

Materials Chemistry · 2016–2022

A researcher in the Science corpus: 13 papers; trusts 68 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 496 citations into it; #1,041 of 11,513 by trust here, #2,742 by citations
• Atomic and Molecular Physics, and Optics — 272 citations into it; #3,672 of 7,135 by trust here, #3,027 by citations
• Computational Theory and Mathematics — 262 citations into it; #3,076 of 12,182 by trust here, #538 by citations
• Computational Chemistry & Drug Discovery — 209 citations into it
• Molecular Biology — 206 citations into it; #4,461 of 17,773 by trust here, #4,657 by citations
• Artificial Intelligence — 157 citations into it
• Physical and Theoretical Chemistry — 128 citations into it
• AI for Physical Sciences — 112 citations into it

Whom Michael Gastegger trusts overall

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

1. J. Behler
2. Kristof T. Schütt
3. Geoffrey E. Hinton
4. Frank Neese
5. Yoshua Bengio
6. Alexandre Tkatchenko
7. A. P. Bart'ok
8. Diederik P. Kingma
9. Yann LeCun
10. Alex Krizhevsky

Who trusts Michael Gastegger overall

1. Pascal Friederich
2. Oliver T. Unke
3. Kenneth Atz
4. Kristof T. Schütt
5. Niklas W. A. Gebauer
6. Florian Hase
7. Stefan Chmiela
8. Julia Westermayr
9. Clemens Isert
10. Justin S. Smith

Papers from Michael Gastegger's perspective

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

Their papers

• Machine learning molecular dynamics for the simulation of infrared spectra (2017) — 389 citations
• Equivariant message passing for the prediction of tensorial properties and molecular spectra (2021) — 803 citations
• Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems (2021) — 675 citations
• SchNetPack: A Deep Learning Toolbox For Atomistic Systems. (2018) — 389 citations
• Inverse design of 3d molecular structures with conditional generative neural networks (2021) — 228 citations
• Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions (2019) — 448 citations
• Machine Learning Force Fields (2020) — 1,403 citations
• Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes. (2016) — 52 citations
• Quantum-chemical insights from interpretable atomistic neural networks (2018) — 35 citations
• Machine learning enables long time scale molecular photodynamics simulations (2018) — 99 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