Raghunathan Ramakrishnan
Raghunathan Ramakrishnan
Reader, TIFR Centre for Interdisciplinary Sciences, Hyderabad, India
確認したメール アドレス: tifrh.res.in - ホームページ
タイトル
引用先
引用先
Quantum chemistry structures and properties of 134 kilo molecules
R Ramakrishnan, PO Dral, M Rupp, OA Von Lilienfeld
Scientific data 1, 140022, 2014
3272014
Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space
K Hansen, F Biegler, R Ramakrishnan, W Pronobis, OA Von Lilienfeld, ...
The journal of physical chemistry letters 6 (12), 2326-2331, 2015
3182015
Big data meets quantum chemistry approximations: The Δ-machine learning approach
R Ramakrishnan, PO Dral, M Rupp, OA von Lilienfeld
Journal of chemical theory and computation 11 (5), 2087-2096, 2015
2272015
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
OA von Lilienfeld, R Ramakrishnan, M Rupp, A Knoll
International Journal of Quantum Chemistry, 2015
1352015
Machine learning for quantum mechanical properties of atoms in molecules
M Rupp, R Ramakrishnan, OA von Lilienfeld
The journal of physical chemistry letters 6 (16), 3309–3313, 2015
1032015
Electronic spectra from TDDFT and machine learning in chemical space
R Ramakrishnan, M Hartmann, E Tapavicza, OA von Lilienfeld
The Journal of chemical physics 143 (8), 084111, 2015
742015
Many molecular properties from one kernel in chemical space
R Ramakrishnan, OA von Lilienfeld
CHIMIA International Journal for Chemistry 69 (4), 182-186, 2015
512015
MACHINE LEARNING, QUANTUM CHEMISTRY, AND CHEMICAL SPACE
R Ramakrishnan, OA von Lilienfeld
Reviews in Computational Chemistry, 2017
382017
Genetic optimization of training sets for improved machine learning models of molecular properties
NJ Browning, R Ramakrishnan, OA Von Lilienfeld, U Roethlisberger
The journal of physical chemistry letters 8 (7), 1351-1359, 2017
372017
Fast and accurate predictions of covalent bonds in chemical space
KYS Chang, S Fias, R Ramakrishnan, OA von Lilienfeld
The Journal of chemical physics 144 (17), 174110, 2016
272016
Generalized density-functional tight-binding repulsive potentials from unsupervised machine learning
JJ Kranz, M Kubillus, R Ramakrishnan, OA von Lilienfeld, M Elstner
Journal of chemical theory and computation 14 (5), 2341-2352, 2018
202018
Sci. Data 1, 140022 (2014)
R Ramakrishnan, PO Dral, M Rupp, OA Von Lilienfeld
202014
The DFT+ U method in the linear combination of Gaussian-type orbitals framework: Role of 4f orbitals in the bonding of LuF3
R Ramakrishnan, AV Matveev, N Rösch
Chemical Physics Letters 468 (4-6), 158-161, 2009
202009
Control and analysis of single-determinant electron dynamics
R Ramakrishnan, M Nest
Physical Review A 85 (5), 054501, 2012
182012
Reviews in Computational Chemistry
R Ramakrishnan, OA von Lilienfeld
Wiley, 2017
172017
Semi-quartic force fields retrieved from multi-mode expansions: Accuracy, scaling behavior, and approximations
R Ramakrishnan, G Rauhut
The Journal of chemical physics 142 (15), 154118, 2015
172015
Manifestation of diamagnetic chemical shifts of proton NMR signals by an anisotropic shielding effect of nitrate anions
HS Sahoo, DK Chand, S Mahalakshmi, MH Mir, R Raghunathan
Tetrahedron letters 48 (5), 761-765, 2007
152007
Electron dynamics across molecular wires: A time-dependent configuration interaction study
R Ramakrishnan, S Raghunathan, M Nest
Chemical Physics 420, 44-49, 2013
112013
Effects of the self-interaction error in Kohn–Sham calculations: A DFT+ U case study on penta-aqua uranyl (VI)
R Ramakrishnan, AV Matveev, N Rösch
Computational and Theoretical Chemistry 963 (2-3), 337-343, 2011
112011
Machine learning, quantum mechanics, and chemical compound space
R Ramakrishnan, OA von Lilienfeld
arXiv preprint arXiv:1510.07512, 2015
92015
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