William H. Green Jr.

Computational Theory and Mathematics · 2004–2021

A researcher in the Science corpus: 13 papers; trusts 11 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 118 citations into it; #2,435 of 12,182 by trust here, #609 by citations
• Materials Chemistry — 97 citations into it; #1,438 of 11,513 by trust here, #3,728 by citations
• Computational Chemistry & Drug Discovery — 88 citations into it; #138 of 425 by trust here, #148 by citations
• Molecular Biology — 70 citations into it; #14,424 of 17,773 by trust here, #8,791 by citations
• Artificial Intelligence — 29 citations into it
• Statistical and Nonlinear Physics — 8 citations into it
• Computational Mechanics — 6 citations into it
• Statistics and Probability — 5 citations into it

Whom William H. Green Jr. trusts overall

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

1. Karen E. Willcox
2. Tan Bui-Thanh
3. G. Berkooz
4. Connor W. Coley
5. Michael H. Abraham
6. Kristof T. Schütt
7. Marwin H. S. Segler

Who trusts William H. Green Jr. overall

1. Philippe Schwaller
2. Philipp Seidl
3. José Jiménez-Luna
4. Connor W. Coley
5. Colin A. Grambow
6. Tânia F Cova
7. Rohit Batra
8. Chaopeng Shen
9. Igor V. Tetko
10. Francesca Grisoni

Papers from William H. Green Jr.'s perspective

Ordered by how strongly the researchers William H. Green Jr. trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• A graph-convolutional neural network model for the prediction of chemical reactivity (2018) — 515 citations
• Prediction of Organic Reaction Outcomes Using Machine Learning (2017) — 654 citations
• GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles (2021) — 176 citations
• Machine Learning in Computer-Aided Synthesis Planning. (2018) — 563 citations
• Using Machine Learning To Predict Suitable Conditions for Organic Reactions (2018) — 334 citations
• Computer-Assisted Retrosynthesis Based on Molecular Similarity (2017) — 381 citations
• SCScore: Synthetic Complexity Learned from a Reaction Corpus (2018) — 326 citations
• Learning only buys you so much: Practical limits on battery price reduction (2019) — 160 citations
• Accurate Thermochemistry with Small Data Sets: A Bond Additivity Correction and Transfer Learning Approach. (2019) — 89 citations
• Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction (2017) — 440 citations
• Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms (2016) — 678 citations
• Using adaptive proper orthogonal decomposition to solve the reaction--diffusion equation (2007) — 55 citations
• High‐gradient magnetic separation of coated magnetic nanoparticles (2004) — 243 citations