R. Ramakrishnan

Materials Chemistry · with Lilienfeld · 2013–2015

A researcher in the Science corpus: 6 papers; trusts 11 researchers and is trusted by 55 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 68 citations into it; #3,727 of 11,513 by trust here, #2,779 by citations
• Atomic and Molecular Physics, and Optics — 65 citations into it; #857 of 7,135 by trust here, #854 by citations
• Computational Chemistry & Drug Discovery — 57 citations into it; #65 of 425 by trust here, #60 by citations
• Computational Theory and Mathematics — 45 citations into it; #799 of 12,182 by trust here, #148 by citations
• Physical and Theoretical Chemistry — 34 citations into it
• Molecular Biology — 25 citations into it; #11,972 of 17,773 by trust here, #6,817 by citations
• Organic Chemistry — 23 citations into it; #82 of 1,495 by trust here, #178 by citations
• AI for Physical Sciences — 21 citations into it

Whom R. Ramakrishnan trusts overall

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

1. Matthias Rupp 
2. David Ruppert
3. James J. P. Stewart
4. Larry A. Curtiss
5. Eric R. Ziegel
6. Grgoire Montavon
7. Lars Ruddigkeit
8. Katja Hansen
9. Rickard Armiento
10. Alexandre Tkatchenko

Who trusts R. Ramakrishnan overall

1. Christopher Morris
2. Tânia F Cova
3. Katsuhiko Ishiguro
4. Minkai Xu
5. Matthew K. Matlock
6. Uri Alon
7. Bidisha Samanta
8. Pavlo O. Dral
9. Benjamín Sánchez-Lengeling
10. Qi Liu

Papers from R. Ramakrishnan's perspective

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

1. The Elements of Statistical Learning
2. I and J
3. Fast and accurate modeling of molecular atomization energies with machine learning.
4. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
5. Virtual screening: an endless staircase?
6. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.
7. Learning with Kernels
8. Chemical space
9. Gradient-based learning applied to document recognition
10. Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

Their papers

• Quantum chemistry structures and properties of 134 kilo molecules (2014) — 2,384 citations
• Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space (2015) — 732 citations
• Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. (2015) — 794 citations
• Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties (2013) — 211 citations
• Electronic spectra from TDDFT and machine learning in chemical space. (2015) — 287 citations
• Machine Learning for Quantum Mechanical Properties of Atoms in Molecules (2015) — 201 citations