Albert Musaelian

Materials Chemistry · 2021–2022

A researcher in the Science corpus: 3 papers; trusts 22 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 79 citations into it; #11,365 of 11,513 by trust here, #7,472 by citations
• Computational Theory and Mathematics — 36 citations into it
• Computational Chemistry & Drug Discovery — 33 citations into it
• Artificial Intelligence — 30 citations into it; #35,219 of 35,345 by trust here, #15,004 by citations
• Molecular Biology — 29 citations into it; #17,641 of 17,773 by trust here, #9,476 by citations
• Atomic and Molecular Physics, and Optics — 28 citations into it
• Model Architectures — 22 citations into it
• Physical and Theoretical Chemistry — 21 citations into it

Whom Albert Musaelian trusts overall

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

1. Kristof T. Schütt
2. Ralf Drautz
3. Stefan Chmiela
4. Diederik P. Kingma
5. A. P. Bart'ok
6. Kaiming He
7. Risi Kondor
8. Johannes Klicpera
9. Aidan P. Thompson
10. Alexander V. Shapeev

Who trusts Albert Musaelian overall

1. Ilyes Batatia
2. Mario Geiger
3. Tess E. Smidt
4. D. Kov'acs
5. Kenneth Atz

Papers from Albert Musaelian's perspective

Ordered by how strongly the researchers Albert Musaelian 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials (2021) — 2,192 citations
• Learning local equivariant representations for large-scale atomistic dynamics (2022) — 820 citations
• The design space of E(3)-equivariant atom-centred interatomic potentials (2022) — 361 citations