Aldo Glielmo

Materials Chemistry · 2016–2021

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 71 citations into it; #4,240 of 11,513 by trust here, #7,401 by citations
• Molecular Biology — 49 citations into it
• Atomic and Molecular Physics, and Optics — 38 citations into it; #3,423 of 7,135 by trust here, #3,907 by citations
• Computational Theory and Mathematics — 34 citations into it; #7,233 of 12,182 by trust here, #4,199 by citations
• Computational Chemistry & Drug Discovery — 26 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Statistics and Probability — 17 citations into it
• AI for Physical Sciences — 15 citations into it

Whom Aldo Glielmo trusts overall

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

1. Radford M. Neal
2. A. P. Bart'ok
3. J. Behler
4. Yuri Mishin
5. Matthias Rupp 
6. Gábor Csányi
7. J. Perdew
8. Frank No'e
9. Danilo Jimenez Rezende
10. Alessandro Laio

Who trusts Aldo Glielmo overall

1. Evgeny V. Podryabinkin
2. G. P. P. Pun
3. Yuri Mishin
4. Rohit Batra
5. Rampi Ramprasad
6. Alexander V. Shapeev
7. Jared D. Willard
8. Stefan Chmiela
9. Oliver T. Unke

Papers from Aldo Glielmo's perspective

Ordered by how strongly the researchers Aldo Glielmo 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
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. Pattern Recognition and Machine Learning

Their papers

• Efficient nonparametric n -body force fields from machine learning (2018) — 107 citations
• Accurate interatomic force fields via machine learning with covariant kernels (2016) — 175 citations
• Unsupervised Learning Methods for Molecular Simulation Data (2021) — 320 citations