Kipton Barros

Materials Chemistry · 2009–2020

A researcher in the Science corpus: 9 papers; trusts 14 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 136 citations into it; #6,644 of 11,513 by trust here, #5,575 by citations
• Computational Theory and Mathematics — 85 citations into it; #9,614 of 12,182 by trust here, #2,351 by citations
• Atomic and Molecular Physics, and Optics — 73 citations into it
• Computational Chemistry & Drug Discovery — 63 citations into it
• Physical and Theoretical Chemistry — 52 citations into it
• AI for Physical Sciences — 37 citations into it
• Artificial Intelligence — 23 citations into it
• Spectroscopy — 15 citations into it

Whom Kipton Barros trusts overall

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

1. J. Behler
2. Frank Neese
3. Justin S. Smith

Who trusts Kipton Barros overall

1. Stefan Chmiela

Papers from Kipton Barros's perspective

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

1. Deep Learning
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. A Survey on Transfer Learning
5. Scikit-learn: Machine Learning in Python
6. Gradient-based learning applied to document recognition
7. Random Forests
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

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
• Extending the applicability of the ANI deep learning molecular potential to Sulfur and Halogens. (2020) — 371 citations
• Learning molecular energies using localized graph kernels. (2016) — 55 citations
• Outsmarting Quantum Chemistry Through Transfer Learning (2018)
• Machine Learning Predicts Laboratory Earthquakes (2017) — 349 citations
• Learning Potential Energy Landscapes using Graph Kernels (2016)
• Solving lattice QCD systems of equations using mixed precision solvers on GPUs (2009) — 506 citations