| FCHL revisited: Faster and more accurate quantum machine learning AS Christensen, LA Bratholm, FA Faber, O Anatole von Lilienfeld The Journal of chemical physics 152 (4), 044107, 2020 | 58 | 2020 |
| Training neural nets to learn reactive potential energy surfaces using interactive quantum chemistry in virtual reality S Amabilino, LA Bratholm, SJ Bennie, AC Vaucher, M Reiher, ... The Journal of Physical Chemistry A 123 (20), 4486-4499, 2019 | 36 | 2019 |
| QML: A Python toolkit for quantum machine learning AS Christensen, FA Faber, B Huang, LA Bratholm, A Tkatchenko, ... URL https://github. com/qmlcode/qml, 2017 | 36* | 2017 |
| Photutils: Photometry tools L Bradley, B Sipocz, T Robitaille, E Tollerud, C Deil, Z Vinícius, K Barbary, ... Astrophysics Source Code Library, ascl: 1609.011, 2016 | 32 | 2016 |
| IMPRESSION–prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy W Gerrard, LA Bratholm, MJ Packer, AJ Mulholland, DR Glowacki, ... Chemical science 11 (2), 508-515, 2020 | 16 | 2020 |
| ProCS15: a DFT-based chemical shift predictor for backbone and Cβ atoms in proteins AS Larsen, LA Bratholm, AS Christensen, M Channir, JH Jensen PeerJ 3, e1344, 2015 | 12 | 2015 |
| Automated Fragmentation Polarizable Embedding Density Functional Theory (PE-DFT) Calculations of Nuclear Magnetic Resonance (NMR) Shielding Constants of Proteins with … C Steinmann, LA Bratholm, JMH Olsen, J Kongsted Journal of chemical theory and computation 13 (2), 525-536, 2017 | 11 | 2017 |
| Low dimensional representations along intrinsic reaction coordinates and molecular dynamics trajectories using interatomic distance matrices SR Hare, LA Bratholm, DR Glowacki, BK Carpenter Chemical science 10 (43), 9954-9968, 2019 | 10 | 2019 |
| Bayesian inference of protein structure from chemical shift data LA Bratholm | 8* | |
| Calculate root-mean-square deviation (RMSD) of two molecules using rotation JC Kromann, L Bratholm Xyz or Pdb Format: Charnley/Rmsd, 2019 | 5 | 2019 |
| Sonifying stochastic walks on biomolecular energy landscapes RE Arbon, AJ Jones, LA Bratholm, T Mitchell, DR Glowacki arXiv preprint arXiv:1803.05805, 2018 | 5 | 2018 |
| Protein structure refinement using a quantum mechanics-based chemical shielding predictor LA Bratholm, JH Jensen Chemical science 8 (3), 2061-2072, 2017 | 5 | 2017 |
| GitHub: Calculate RMSD for two XYZ structures JC Kromann, L Bratholm | 3 | 2016 |
| Training atomic neural networks using fragment-based data generated in virtual reality S Amabilino, LA Bratholm, SJ Bennie, MB O’Connor, DR Glowacki The Journal of Chemical Physics 153 (15), 154105, 2020 | 1 | 2020 |
| A community-powered search of machine learning strategy space to find NMR property prediction models LA Bratholm, W Gerrard, B Anderson, S Bai, S Choi, L Dang, P Hanchar, ... arXiv preprint arXiv:2008.05994, 2020 | 1 | 2020 |
| Protein Structure Validation and Refinement Using Chemical Shifts Derived from Quantum Mechanics LA Bratholm University of Copenhagen, Faculty of Science, Department of Chemistry, 2016 | | 2016 |
| Computational Assignment of Chemical Shifts for Protein Residues LA Bratholm arXiv preprint arXiv:1311.3186, 2013 | | 2013 |
Anders S. ChristensenQuantum Consulting by Christensenunibas.ch üzerinde doğrulanmış e-posta adresine sahip
