Roman I. Zubatyuk

Materials Chemistry · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 77 citations into it
• Atomic and Molecular Physics, and Optics — 46 citations into it
• Computational Chemistry & Drug Discovery — 32 citations into it
• AI for Physical Sciences — 23 citations into it
• Physical and Theoretical Chemistry — 23 citations into it
• Artificial Intelligence — 12 citations into it
• Spectroscopy — 11 citations into it

Whom Roman I. Zubatyuk trusts overall

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

1. Frank Neese
2. Sinno Jialin Pan
3. Matthew E. Taylor
4. Justin S. Smith
5. Qiang Yang
6. J. Behler
7. Christoph Riplinger

Papers from Roman I. Zubatyuk's perspective

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

1. Deep Learning
2. A Survey on Transfer Learning
3. Transfer Learning for Reinforcement Learning Domains: A Survey
4. Boosting for transfer learning
5. Quantum-chemical insights from deep tensor neural networks
6. ImageNet classification with deep convolutional neural networks
7. Gradient-based learning applied to document recognition
8. Machine learning: Trends, perspectives, and prospects
9. Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.
10. The ORCA program system

Their papers

• Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning (2019) — 591 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)