Adrian E. Roitberg
Materials Chemistry · 2000–2020
A researcher in the Science corpus: 14 papers; trusts 17 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 213 citations into it; #1,639 of 11,513 by trust here, #2,027 by citations
- Molecular Biology — 154 citations into it
- Computational Theory and Mathematics — 148 citations into it; #3,190 of 12,182 by trust here, #396 by citations
- Atomic and Molecular Physics, and Optics — 108 citations into it; #2,461 of 7,135 by trust here, #854 by citations
- Computational Chemistry & Drug Discovery — 93 citations into it; #178 of 425 by trust here, #69 by citations
- Physical and Theoretical Chemistry — 60 citations into it
- Spectroscopy — 41 citations into it
- AI for Physical Sciences — 36 citations into it; #135 of 247 by trust here, #49 by citations
Whom Adrian E. Roitberg trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Adrian E. Roitberg overall
Papers from Adrian E. Roitberg's perspective
Ordered by how strongly the researchers Adrian E. Roitberg trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Learning
- Et al
- Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
- A Survey on Transfer Learning
- Information Theory: 1948-1998 - Guest Editorial
- Transfer Learning for Reinforcement Learning Domains: A Survey
- A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules J. Am. Chem. Soc. 1995, 117, 5179−5197
- The Amber biomolecular simulation programs
- Quantum-chemical insights from deep tensor neural networks
- Boosting for transfer learning
Their papers
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost (2016) — 1,346 citations
- Comparison of multiple Amber force fields and development of improved protein backbone parameters (2006) — 6,725 citations
- Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning (2019) — 591 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
- QSAR without borders (2020) — 546 citations
- Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. (2015) — 1,251 citations
- Extending the applicability of the ANI deep learning molecular potential to Sulfur and Halogens. (2020) — 371 citations
- Pulsed terahertz spectroscopy of DNA, bovine serum albumin and collagen between 0.1 and 2.0 THz (2000) — 695 citations
- MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. (2012) — 4,086 citations
- Bounds on the Jensen Gap, and Implications for Mean-Concentrated Distributions (2017) — 75 citations
- GPU-accelerated molecular dynamics and free energy methods in Amber18: performance enhancements and new features (2018) — 423 citations
- Outsmarting Quantum Chemistry Through Transfer Learning (2018)
- Molecular wire conductance: Electrostatic potential spatial profile (2000) — 163 citations