Raghunathan Ramakrishnan
Raghunathan Ramakrishnan
Reader, TIFR Centre for Interdisciplinary Sciences, Hyderabad, India
Verified email at tifrh.res.in - Homepage
Title
Cited by
Cited by
Year
Quantum chemistry structures and properties of 134 kilo molecules
R Ramakrishnan, PO Dral, M Rupp, OA Von Lilienfeld
Scientific data 1 (1), 1-7, 2014
4132014
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
3822015
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
2612015
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
1522015
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
1232015
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
872015
Many molecular properties from one kernel in chemical space
R Ramakrishnan, OA von Lilienfeld
CHIMIA International Journal for Chemistry 69 (4), 182-186, 2015
562015
MACHINE LEARNING, QUANTUM CHEMISTRY, AND CHEMICAL SPACE
R Ramakrishnan, OA von Lilienfeld
Reviews in Computational Chemistry, 2017
472017
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
402017
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
312016
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
222018
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
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
182015
Control and analysis of single-determinant electron dynamics
R Ramakrishnan, M Nest
Physical Review A 85 (5), 054501, 2012
182012
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
162007
Electron dynamics across molecular wires: A time-dependent configuration interaction study
R Ramakrishnan, S Raghunathan, M Nest
Chemical Physics 420, 44-49, 2013
122013
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
Reviews in Computational Chemistry
R Ramakrishnan, OA Von Lilienfeld
Wiley, 2017
92017
Charge transfer dynamics from adsorbates to surfaces with single active electron and configuration interaction based approaches
R Ramakrishnan, M Nest
Chemical Physics 446, 24-29, 2015
82015
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