Seyed Mohamad Moosavi

Materials Chemistry · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 17 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 205 citations into it
• Computational Theory and Mathematics — 100 citations into it
• Artificial Intelligence — 86 citations into it
• Inorganic Chemistry — 84 citations into it; #608 of 820 by trust here, #680 by citations
• Computational Chemistry & Drug Discovery — 40 citations into it
• Molecular Biology — 36 citations into it
• Computer Vision and Pattern Recognition — 28 citations into it
• Atomic and Molecular Physics, and Optics — 25 citations into it

Whom Seyed Mohamad Moosavi trusts overall

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

1. Frank No'e
2. Cynthia Rudin
3. J. Behler
4. Marwin H. S. Segler
5. Matt J. Kusner
6. Kevin Maik Jablonka
7. Dongkun Zhang
8. Andreas Mardt
9. Geoffrey E. Hinton
10. Diederik P. Kingma

Who trusts Seyed Mohamad Moosavi overall

1. Kevin Maik Jablonka

Papers from Seyed Mohamad Moosavi's perspective

Ordered by how strongly the researchers Seyed Mohamad Moosavi 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Big-Data Science in Porous Materials: Materials Genomics and Machine Learning (2020) — 469 citations
• Understanding the diversity of the metal-organic framework ecosystem (2020) — 487 citations
• The Role of Machine Learning in the Understanding and Design of Materials (2020) — 345 citations
• Data-driven design of metal–organic frameworks for wet flue gas CO2 capture (2019) — 689 citations