Olexandr Isayev

Materials Chemistry · 2016–2021

A researcher in the Science corpus: 11 papers; trusts 35 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 273 citations into it; #725 of 11,513 by trust here, #1,379 by citations
• Computational Theory and Mathematics — 198 citations into it; #1,343 of 12,182 by trust here, #129 by citations
• Computational Chemistry & Drug Discovery — 131 citations into it; #217 of 425 by trust here, #122 by citations
• Molecular Biology — 131 citations into it; #8,381 of 17,773 by trust here, #4,989 by citations
• Atomic and Molecular Physics, and Optics — 101 citations into it; #3,144 of 7,135 by trust here, #1,491 by citations
• Physical and Theoretical Chemistry — 66 citations into it
• Artificial Intelligence — 57 citations into it
• AI for Physical Sciences — 48 citations into it; #137 of 247 by trust here, #49 by citations

Whom Olexandr Isayev trusts overall

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

1. Ronald J. Williams
2. Alexander Tropsha
3. Noel M. O'Boyle
4. J. Behler
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Ilya Sutskever
8. J. Urgen Schmidhuber
9. Peter Ertl
10. Diederik P. Kingma

Who trusts Olexandr Isayev overall

1. Frank No'e
2. Kyle Swanson
3. Chi Chen
4. Kamal Choudhary
5. Justin S. Smith
6. Wenhao Gao
7. José Jiménez-Luna
8. Garrett B. Goh
9. Tânia F Cova
10. Pascal Friederich

Papers from Olexandr Isayev's perspective

Ordered by how strongly the researchers Olexandr Isayev trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost (2016) — 1,346 citations
• Deep reinforcement learning for de novo drug design (2017) — 1,235 citations
• Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning (2019) — 591 citations
• Machine learning for molecular and materials science (2018) — 3,942 citations
• Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks. (2018) — 105 citations
• Less is more: sampling chemical space with active learning (2018) — 750 citations
• Universal fragment descriptors for predicting properties of inorganic crystals (2016) — 599 citations
• Best practices in machine learning for chemistry (2021) — 384 citations
• QSAR without borders (2020) — 546 citations
• Extending the applicability of the ANI deep learning molecular potential to Sulfur and Halogens. (2020) — 371 citations
• Outsmarting Quantum Chemistry Through Transfer Learning (2018)