Nicholas Lubbers

Materials Chemistry · 2012–2019

A researcher in the Science corpus: 7 papers; trusts 9 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 111 citations into it
• Atomic and Molecular Physics, and Optics — 63 citations into it; #6,087 of 7,135 by trust here, #2,263 by citations
• Computational Chemistry & Drug Discovery — 56 citations into it
• Physical and Theoretical Chemistry — 41 citations into it
• AI for Physical Sciences — 29 citations into it
• Artificial Intelligence — 21 citations into it
• Spectroscopy — 17 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Nicholas Lubbers trusts overall

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

1. J. Behler
2. Frank Neese
3. Justin S. Smith
4. Matthias Rupp 
5. Sander Dieleman
6. R. Ramakrishnan
7. Diederik P. Kingma
8. Geoffrey E. Hinton
9. Sinno Jialin Pan
10. Yann LeCun

Who trusts Nicholas Lubbers overall

1. Ilyes Batatia
2. Justin S. Smith
3. Pascal Friederich
4. Oliver T. Unke
5. Kristof T. Schütt
6. B. Nebgen
7. D. Kov'acs
8. Pavlo O. Dral
9. L. Strigari

Papers from Nicholas Lubbers's perspective

Ordered by how strongly the researchers Nicholas Lubbers trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Deep Learning
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning (2019) — 591 citations
• Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks. (2018) — 105 citations
• Hierarchical modeling of molecular energies using a deep neural network. (2017) — 312 citations
• Less is more: sampling chemical space with active learning (2018) — 750 citations
• Outsmarting Quantum Chemistry Through Transfer Learning (2018)
• Machine Learning Predicts Laboratory Earthquakes (2017) — 349 citations
• The Effective Field Theory of Dark Matter Direct Detection (2012) — 562 citations