フォロー
Lars Andersen Bratholm
Lars Andersen Bratholm
確認したメール アドレス: bristol.ac.uk
タイトル
引用先
引用先
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
2822020
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
882017
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
862019
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
742020
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
742016
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
562019
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
212017
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
192020
Calculate root-mean-square deviation (RMSD) of two molecules using rotation
JC Kromann
Github, Dataset. https://github. com/charnley/rmsd, 2019
192019
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
162015
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
142021
Bayesian inference of protein structure from chemical shift data
LA Bratholm
14*
Protein structure refinement using a quantum mechanics-based chemical shielding predictor
LA Bratholm, JH Jensen
Chemical Science 8 (3), 2061-2072, 2017
112017
Sonifying stochastic walks on biomolecular energy landscapes
RE Arbon, AJ Jones, LA Bratholm, T Mitchell, DR Glowacki
arXiv preprint arXiv:1803.05805, 2018
102018
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
72020
Faber FA and von Lilienfeld OA
AS Christensen, LA Bratholm
J. Chem. Phys. 2019, 150, 2019
52019
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
32016
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
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論文 1–20