Paul L. A. Popelier
Atomic and Molecular Physics, and Optics · 2009–2015
A researcher in the Science corpus: 5 papers; trusts 2 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Atomic and Molecular Physics, and Optics — 16 citations into it; #3,213 of 7,135 by trust here, #5,156 by citations
- Materials Chemistry — 12 citations into it; #1,861 of 11,513 by trust here, #5,658 by citations
- Molecular Biology — 7 citations into it; #4,912 of 17,773 by trust here, #7,747 by citations
Whom Paul L. A. Popelier trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Paul L. A. Popelier overall
Papers from Paul L. A. Popelier's perspective
Ordered by how strongly the researchers Paul L. A. Popelier trusts endorse each paper. Same corpus, a different order for every researcher.
- The Theory of Intermolecular Forces
- The Elements of Statistical Learning
- Comparison of simple potential functions for simulating liquid water
- Theory of intermolecular forces
- Force Field Validation Using Protein Side Chain Prediction
- Self-Consistent Equations Including Exchange and Correlation Effects
- Data Warehouse Tuning: The Supremacy of Bitmap Index
- Modern quantum chemistry : introduction to advanced electronic structure theory
- New spherical‐cutoff methods for long‐range forces in macromolecular simulation
- Efficient particle-mesh Ewald based approach to fixed and induced dipolar interactions
Their papers
- Intramolecular polarisable multipolar electrostatics from the machine learning method Kriging (2011) — 63 citations
- Potential energy surfaces fitted by artificial neural networks. (2010) — 296 citations
- Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning. (2009) — 65 citations
- QCTFF: on the construction of a novel protein force field (2015) — 85 citations
- Prediction of Intramolecular Polarization of Aromatic Amino Acids Using Kriging Machine Learning. (2014) — 40 citations