Keith T. Butler
Materials Chemistry · 2014–2021
A researcher in the Science corpus: 9 papers; trusts 7 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 212 citations into it; #6,584 of 11,513 by trust here, #2,128 by citations
- Electrical and Electronic Engineering — 135 citations into it; #5,379 of 16,539 by trust here, #1,346 by citations
- Renewable Energy, Sustainability and the Environment — 28 citations into it
- Artificial Intelligence — 26 citations into it
- Computational Theory and Mathematics — 25 citations into it; #11,209 of 12,182 by trust here, #949 by citations
- Computational Chemistry & Drug Discovery — 20 citations into it
- Safety, Robustness & Interpretability — 5 citations into it
Whom Keith T. Butler trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Marwin H. S. Segler
- John Shawe-Taylor
- Aram Wettroth Harrow
- Aron Walsh
- Ian J. Goodfellow
- Felix Brockherde
- N. Park
- Alexander Tropsha
- Mark P. Waller
- Jarvist Moore Frost
Who trusts Keith T. Butler overall
- Nicholas J. Schork
- R. Brandt
- Tânia F Cova
- Rohit Batra
- Pascal Friederich
- Jarvist Moore Frost
- Feng Xia
- A. Pais
- Aron Walsh
- V. Stevanovi'c
Papers from Keith T. Butler's perspective
Ordered by how strongly the researchers Keith T. Butler trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Neural Turing Machines
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Atomistic Origins of High-Performance in Hybrid Halide Perovskite Solar Cells (2014) — 2,176 citations
- Machine learning for molecular and materials science (2018) — 3,942 citations
- Molecular ferroelectric contributions to anomalous hysteresis in hybrid perovskite solar cells (2014) — 519 citations
- Ferroelectric materials for solar energy conversion: photoferroics revisited (2014) — 332 citations
- Best practices in machine learning for chemistry (2021) — 384 citations
- Electronic structure of hybrid halide perovskite photovoltaic absorbers (2014) — 547 citations
- Interpretable and Explainable Machine Learning for Materials Science and Chemistry (2021) — 311 citations
- Mutual Insight on Ferroelectrics and Hybrid Halide Perovskites: A Platform for Future Multifunctional Energy Conversion (2017) — 595 citations
- Band alignment of the hybrid halide perovskites CH3NH3PbCl3, CH3NH3PbBr3 and CH3NH3PbI3 (2015) — 149 citations