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), 2020 | 288 | 2020 |
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 | 88 | 2017 |
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 | 87 | 2019 |
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 | 77 | 2020 |
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 | 75 | 2016 |
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 | 57 | 2019 |
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 | 22 | 2017 |
Calculate root-mean-square deviation (RMSD) of two molecules using rotation JC Kromann Github, Dataset. https://github. com/charnley/rmsd, 2019 | 21 | 2019 |
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), 2020 | 19 | 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 | 16 | 2015 |
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, ... Plos one 16 (7), e0253612, 2021 | 14 | 2021 |
Bayesian inference of protein structure from chemical shift data LA Bratholm | 14* | |
Sonifying stochastic walks on biomolecular energy landscapes RE Arbon, AJ Jones, LA Bratholm, T Mitchell, DR Glowacki arXiv preprint arXiv:1803.05805, 2018 | 11 | 2018 |
Protein structure refinement using a quantum mechanics-based chemical shielding predictor LA Bratholm, JH Jensen Chemical Science 8 (3), 2061-2072, 2017 | 11 | 2017 |
Anatole von Lilienfeld, O. FCHL revisited: faster and more accurate quantum machine learning AS Christensen, LA Bratholm, FA Faber J. Chem. Phys 152, 044107, 2020 | 7 | 2020 |
Faber FA and von Lilienfeld OA AS Christensen, LA Bratholm J. Chem. Phys. 2019, 150, 2019 | 5 | 2019 |
Calculate Root-mean-square deviation (RMSD) of Two Molecules Using Rotation J Charnley, L Bratholm GitHub 1, 2, 0 | 5 | |
GitHub: Calculate RMSD for two XYZ structures JC Kromann, L Bratholm | 3 | 2016 |
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 |