Dane Morgan

Materials Chemistry · npj Computational Materials · 2000–2020

A researcher in the Science corpus: 24 papers; trusts 48 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 300 citations into it; #2,511 of 11,513 by trust here, #1,675 by citations
• Electrical and Electronic Engineering — 140 citations into it; #5,417 of 16,539 by trust here, #1,301 by citations
• Renewable Energy, Sustainability and the Environment — 95 citations into it; #4,937 of 5,212 by trust here, #1,233 by citations
• Electronic, Optical and Magnetic Materials — 80 citations into it; #2,155 of 2,886 by trust here, #1,035 by citations
• Atomic and Molecular Physics, and Optics — 55 citations into it; #6,873 of 7,135 by trust here, #2,988 by citations
• Mechanical Engineering — 50 citations into it; #6,921 of 8,300 by trust here, #7,261 by citations
• Computational Theory and Mathematics — 35 citations into it
• Artificial Intelligence — 31 citations into it

Whom Dane Morgan trusts overall

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

1. Logan T. Ward
2. Benjamín Sánchez-Lengeling
3. R. Jose
4. J. Behler
5. Rampi Ramprasad
6. Chris Wolverton
7. Alán Aspuru-Guzik
8. D. Jiang
9. Seeram Ramakrishna
10. David A. Ferrucci

Who trusts Dane Morgan overall

1. D. Tourret
2. Gerbrand Ceder
3. Shidong Wang
4. Stefano Curtarolo
5. Anubhav Jain
6. Tim Mueller
7. Hong Liu
8. Geoffroy Hautier
9. Ryan Jacobs
10. Y. Meng

Papers from Dane Morgan's perspective

Ordered by how strongly the researchers Dane Morgan 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• AFLOW: An automatic framework for high-throughput materials discovery (2012) — 1,411 citations
• Accuracy of ab initio methods in predicting the crystal structures of metals: A review of 80 binary alloys (2005) — 294 citations
• Predicting crystal structure by merging data mining with quantum mechanics (2006) — 436 citations
• Li Conductivity in Li x MPO 4 ( M = Mn , Fe , Co , Ni ) Olivine Materials (2004) — 1,015 citations
• The electronic structure and band gap of LiFePO4 and LiMnPO4 (2004) — 374 citations
• 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
• First-principles study of native point defects in ZnO (2000) — 1,548 citations
• Instability of Pt ∕ C Electrocatalysts in Proton Exchange Membrane Fuel Cells A Mechanistic Investigation (2005) — 1,373 citations
• Material Discovery and Design Principles for Stable, High Activity Perovskite Cathodes for Solid Oxide Fuel Cells (2018) — 182 citations
• Nondilute diffusion from first principles: Li diffusion in Li x TiS 2 (2008) — 214 citations
• Ab initio energetics of LaBO3(001) (B=Mn, Fe, Co, and Ni) for solid oxide fuel cell cathodes (2009) — 369 citations
• Ab initio-based diffusion theory and tracer diffusion in Ni–Cr and Ni–Fe alloys (2010) — 151 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
• Assessment of radiation-induced segregation mechanisms in austenitic and ferritic–martensitic alloys (2011) — 148 citations
• Pt nanoparticle stability in PEM fuel cells: influence of particle size distribution and crossover hydrogen (2009) — 302 citations
• Phase Stability of Nickel Hydroxides and Oxyhydroxides (2006) — 205 citations