Logan T. Ward

Materials Chemistry · 2016–2022

A researcher in the Science corpus: 7 papers; trusts 13 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 136 citations into it; #4,642 of 11,513 by trust here, #3,505 by citations
• Inorganic Chemistry — 38 citations into it
• Computational Theory and Mathematics — 35 citations into it; #7,554 of 12,182 by trust here, #1,936 by citations
• Computational Chemistry & Drug Discovery — 26 citations into it
• Atomic and Molecular Physics, and Optics — 25 citations into it

Whom Logan T. Ward trusts overall

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

1. Matthias Rupp 
2. James E. Saal
3. Felix A. Faber
4. Trevor J. Hastie
5. S. Kirklin
6. Ghanshyam Pilania
7. A. P. Bart'ok
8. Ilya Sutskever
9. Geoffrey E. Hinton
10. Bryce Meredig

Who trusts Logan T. Ward overall

1. Dane Morgan
2. Zheng Xiong
3. Ryan Jacobs
4. Kamal Choudhary
5. Yuxin Cui
6. Wei Li
7. Peter Bjørn Jørgensen
8. Chi Chen
9. Brian L. DeCost
10. Valentin G. Stanev

Papers from Logan T. Ward's perspective

Ordered by how strongly the researchers Logan T. Ward 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. Attention is All you Need (Neural Information Processing Systems)
4. Gradient-based learning applied to document recognition
5. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials (2016) — 1,611 citations
• Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations (2017) — 297 citations
• GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics (2022) — 142 citations
• ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition (2018) — 460 citations
• Matminer: An open source toolkit for materials data mining (2018) — 925 citations
• Atomistic calculations and materials informatics: A review (2017) — 217 citations
• Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments (2018) — 474 citations