Ryan Jacobs

Materials Chemistry · 2016–2020

A researcher in the Science corpus: 7 papers; trusts 41 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 249 citations into it; #9,435 of 11,513 by trust here, #7,558 by citations
• Electrical and Electronic Engineering — 89 citations into it; #14,262 of 16,539 by trust here, #10,154 by citations
• Renewable Energy, Sustainability and the Environment — 72 citations into it; #4,938 of 5,212 by trust here, #3,424 by citations
• Electronic, Optical and Magnetic Materials — 62 citations into it
• Mechanical Engineering — 38 citations into it
• Computational Theory and Mathematics — 35 citations into it
• Artificial Intelligence — 30 citations into it
• Computational Chemistry & Drug Discovery — 24 citations into it

Whom Ryan Jacobs trusts overall

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

1. Logan T. Ward
2. Jeff Greeley
3. John H. Booske
4. Jin Suntivich
5. Anubhav Jain
6. Benjamín Sánchez-Lengeling
7. S. Haile
8. R. Jose
9. Geoffroy Hautier
10. A. Jacobson

Who trusts Ryan Jacobs overall

1. Wei Li
2. Jonathan Hwang
3. Dane Morgan
4. D. Tourret
5. K. Exner

Papers from Ryan Jacobs's perspective

Ordered by how strongly the researchers Ryan Jacobs 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Understanding and Controlling the Work Function of Perovskite Oxides Using Density Functional Theory (2016) — 173 citations
• Predicting the thermodynamic stability of perovskite oxides using machine learning models (2018) — 222 citations
• Material Discovery and Design Principles for Stable, High Activity Perovskite Cathodes for Solid Oxide Fuel Cells (2018) — 182 citations
• Opportunities and Challenges for Machine Learning in Materials Science (2020) — 444 citations
• Assessing Correlations of Perovskite Catalytic Performance with Electronic Structure Descriptors (2019) — 157 citations
• Tuning perovskite oxides by strain: Electronic structure, properties, and functions in (electro)catalysis and ferroelectricity (2019) — 233 citations
• New frontiers for the materials genome initiative (2019) — 487 citations