| Semiempirical Quantum Mechanical Methods for Noncovalent Interactions for Chemical and Biochemical Applications AS Christensen, T Kubař, Q Cui, M Elstner Chemical reviews 116 (9), 5301-5337, 2016 | 199 | 2016 |
| Alchemical and structural distribution based representation for universal quantum machine learning FA Faber, AS Christensen, B Huang, OA von Lilienfeld The Journal of Chemical Physics 148 (24), 241717, 2018 | 190 | 2018 |
| 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 | 67 | 2020 |
| Operators in quantum machine learning: Response properties in chemical space AS Christensen, FA Faber, OA von Lilienfeld The Journal of Chemical Physics 150 (6), 064105, 2019 | 66 | 2019 |
| A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules L Cheng, M Welborn, AS Christensen, TF Miller III The Journal of Chemical Physics 150 (13), 131103, 2019 | 60 | 2019 |
| Improving intermolecular interactions in DFTB3 using extended polarization from chemical-potential equalization AS Christensen, M Elstner, Q Cui The Journal of chemical physics 143 (8), 084123, 2015 | 48 | 2015 |
| 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 | 37* | 2017 |
| PHAISTOS: A framework for Markov Chain Monte Carlo simulation and inference of protein structure W Boomsma, J Frellsen, T Harder, S Bottaro, KE Johansson, P Tian, ... Journal of computational chemistry 34 (19), 1697-1705, 2013 | 36 | 2013 |
| A third-generation dispersion and third-generation hydrogen bonding corrected PM6 method: PM6-D3H+ JC Kromann, AS Christensen, C Steinmann, M Korth, JH Jensen PeerJ 2, e449, 2014 | 32 | 2014 |
| DFTB3 parametrization for copper: the importance of orbital angular momentum dependence of hubbard parameters M Gaus, H Jin, D Demapan, AS Christensen, P Goyal, M Elstner, Q Cui Journal of chemical theory and computation 11 (9), 4205-4219, 2015 | 31 | 2015 |
| Protein structure validation and refinement using amide proton chemical shifts derived from quantum mechanics AS Christensen, TE Linnet, M Borg, W Boomsma, K Lindorff-Larsen, ... PLoS One 8 (12), e84123, 2013 | 24 | 2013 |
| Definitive benchmark study of ring current effects on amide proton chemical shifts AS Christensen, SPA Sauer, JH Jensen Journal of Chemical Theory and Computation 7 (7), 2078-2084, 2011 | 20 | 2011 |
| Interface of the polarizable continuum model of solvation with semi-empirical methods in the GAMESS program C Steinmann, KL Blædel, AS Christensen, JH Jensen PloS one 8 (7), e67725, 2013 | 19 | 2013 |
| Neural networks and kernel ridge regression for excited states dynamics of CH2NH: From single-state to multi-state representations and multi-property machine learning models J Westermayr, FA Faber, AS Christensen, OA von Lilienfeld, ... Machine Learning: Science and Technology 1 (2), 025009, 2020 | 18 | 2020 |
| Towards a barrier height benchmark set for biologically relevant systems JC Kromann, AS Christensen, Q Cui, JH Jensen PeerJ 4, e1994, 2016 | 15 | 2016 |
| 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 |
| On the role of gradients for machine learning of molecular energies and forces AS Christensen, OA von Lilienfeld Machine Learning: Science and Technology 1 (4), 045018, 2020 | 11 | 2020 |
| An assessment of the structural resolution of various fingerprints commonly used in machine learning B Parsaeifard, DS De, AS Christensen, FA Faber, E Kocer, S De, J Behler, ... Machine Learning: Science and Technology, 2020 | 10 | 2020 |
| Random versus Systematic Errors in Reaction Enthalpies Computed Using Semiempirical and Minimal Basis Set Methods JC Kromann, A Welford, AS Christensen, JH Jensen ACS Omega 3 (4), 4372-4377, 2018 | 10 | 2018 |
| Bayesian inference of protein structure from chemical shift data LA Bratholm, AS Christensen, T Hamelryck, JH Jensen PeerJ 3, e861, 2015 | 10 | 2015 |
O. Anatole von LilienfeldProfessor at Faculty of Physics, University of ViennaVerified email at univie.ac.at
