O. V. Lilienfeld

Materials Chemistry · 2009–2019

A researcher in the Science corpus: 12 papers; trusts 21 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 186 citations into it; #1,335 of 11,513 by trust here, #1,432 by citations
• Atomic and Molecular Physics, and Optics — 130 citations into it; #495 of 7,135 by trust here, #405 by citations
• Computational Theory and Mathematics — 109 citations into it; #868 of 12,182 by trust here, #97 by citations
• Computational Chemistry & Drug Discovery — 106 citations into it; #34 of 425 by trust here, #15 by citations
• Physical and Theoretical Chemistry — 74 citations into it; #168 of 671 by trust here, #26 by citations
• Molecular Biology — 59 citations into it; #17,078 of 17,773 by trust here, #5,350 by citations
• AI for Physical Sciences — 47 citations into it
• Artificial Intelligence — 47 citations into it

Whom O. V. Lilienfeld trusts overall

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

1. J. Behler
2. Matthias Rupp 
3. Yann LeCun
4. Alexandre Tkatchenko
5. J. Urgen Schmidhuber
6. Geoffrey E. Hinton
7. Kristof T. Schütt
8. R. Ramakrishnan
9. Christopher K. I. Williams
10. Grgoire Montavon

Who trusts O. V. Lilienfeld overall

1. Oliver T. Unke
2. Tânia F Cova
3. Kristof T. Schütt
4. Bing Huang
5. Felix A. Faber
6. Christopher Morris
7. Benjamín Sánchez-Lengeling
8. R. Ramakrishnan
9. Kenneth Atz
10. von Lilienfeld

Papers from O. V. Lilienfeld's perspective

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

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

Their papers

• Quantum chemistry structures and properties of 134 kilo molecules (2014) — 2,384 citations
• Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. (2013) — 563 citations
• Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space (2015) — 732 citations
• Machine learning of molecular electronic properties in chemical compound space (2013) — 659 citations
• Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. (2017) — 593 citations
• Electronic spectra from TDDFT and machine learning in chemical space. (2015) — 287 citations
• The "DNA" of chemistry: Scalable quantum machine learning with "amons" (2017) — 38 citations
• Crystal structure representations for machine learning models of formation energies (2015) — 421 citations
• Exploring chemical compound space with quantum-based machine learning (2019) — 330 citations
• Long Range Interactions in Nanoscale Science. (2010) — 353 citations
• Machine learning prediction errors better than DFT accuracy (2017) — 36 citations
• Accurate ab initio energy gradients in chemical compound space (2009) — 39 citations