O. A. von Lilienfeld

Atomic and Molecular Physics, and Optics · 2006–2017

A researcher in the Science corpus: 10 papers; trusts 20 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Atomic and Molecular Physics, and Optics — 74 citations into it; #387 of 7,135 by trust here, #372 by citations
• Materials Chemistry — 67 citations into it; #574 of 11,513 by trust here, #1,791 by citations
• Computational Chemistry & Drug Discovery — 46 citations into it; #61 of 425 by trust here, #51 by citations
• Physical and Theoretical Chemistry — 46 citations into it; #181 of 671 by trust here, #48 by citations
• Computational Theory and Mathematics — 43 citations into it; #2,169 of 12,182 by trust here, #348 by citations
• Molecular Biology — 27 citations into it; #12,561 of 17,773 by trust here, #12,742 by citations
• AI for Physical Sciences — 20 citations into it; #211 of 247 by trust here, #164 by citations
• Organic Chemistry — 16 citations into it; #1,495 of 1,495 by trust here, #1,002 by citations

Whom O. A. von Lilienfeld trusts overall

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

1. Alexandre Tkatchenko
2. Robert A. Distasio Jr.
3. Eric R. Ziegel
4. Stefan Grimme
5. Matthias Scheffler
6. Yan Zhao
7. J. Perdew
8. Axel D. Becke
9. Donald G. Truhlar
10. Roberto Car

Who trusts O. A. von Lilienfeld overall

1. Tânia F Cova
2. Oliver T. Unke
3. T. Bereau
4. Matthias Rupp 
5. O. V. Lilienfeld
6. Grgoire Montavon
7. Bing Huang
8. von Lilienfeld
9. Matthew J. Hirn
10. Justin Gilmer

Papers from O. A. von Lilienfeld's perspective

Ordered by how strongly the researchers O. A. von Lilienfeld trusts endorse each paper. Same corpus, a different order for every researcher.

1. The Elements of Statistical Learning
2. I and J
3. Modern quantum chemistry : introduction to advanced electronic structure theory
4. “Model”
5. Polymorphism in Molecular Crystals
6. Evidence for van der Waals adhesion in gecko setae (Proceedings of the National Academy of Sciences of the United States of America)
7. Theory of intermolecular forces
8. (IEEE Transactions on Neural Networks,12(6):1498-1504)A Remote Password Authentication Scheme for Multi-Server Architecture Using Neural Network
9. Tkatchenko-Scheffler van der Waals correction method with and without self-consistent screening applied to solids
10. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman

Their papers

• Fast and accurate modeling of molecular atomization energies with machine learning. (2011) — 1,758 citations
• Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity. (2016) — 280 citations
• Alchemical and structural distribution based representation for universal quantum machine learning. (2017) — 323 citations
• Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) Crystals. (2015) — 305 citations
• Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning. (2017) — 165 citations
• Machine Learning for Quantum Mechanical Properties of Atoms in Molecules (2015) — 201 citations
• Toward transferable interatomic van der Waals interactions without electrons: the role of multipole electrostatics and many-body dispersion. (2014) — 18 citations
• Collective many-body van der Waals interactions in molecular systems (2012) — 184 citations
• Library of dispersion-corrected atom-centered potentials for generalized gradient approximation functionals: Elements H, C, N, O, He, Ne, Ar, and Kr (2007) — 136 citations
• Molecular grand-canonical ensemble density functional theory and exploration of chemical space. (2006) — 96 citations