Resilience of Hund's rule in the chemical space of small organic molecules
Majumdar, Atreyee, Ramakrishnan, Raghunathan
Published in Physical chemistry chemical physics : PCCP (22.05.2024)
Published in Physical chemistry chemical physics : PCCP (22.05.2024)
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Journal Article
Electronic spectra from TDDFT and machine learning in chemical space
Ramakrishnan, Raghunathan, Hartmann, Mia, Tapavicza, Enrico, von Lilienfeld, O Anatole
Published in The Journal of chemical physics (28.08.2015)
Published in The Journal of chemical physics (28.08.2015)
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Journal Article
Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
Rupp, Matthias, Ramakrishnan, Raghunathan, von Lilienfeld, O. Anatole
Published in The journal of physical chemistry letters (20.08.2015)
Published in The journal of physical chemistry letters (20.08.2015)
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Journal Article
Structure prediction from spectra amidst dynamical heterogeneity in melanin
Choudhury, Arpan, Ramakrishnan, Raghunathan, Ghosh, Debashree
Published in Chemical communications (Cambridge, England) (29.02.2024)
Published in Chemical communications (Cambridge, England) (29.02.2024)
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Journal Article
Troubleshooting unstable molecules in chemical space
Senthil, Salini, Chakraborty, Sabyasachi, Ramakrishnan, Raghunathan
Published in Chemical science (Cambridge) (21.04.2021)
Published in Chemical science (Cambridge) (21.04.2021)
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Journal Article
Machine learning modeling of Wigner intracule functionals for two electrons in one-dimension
Bhavsar, Rutvij, Ramakrishnan, Raghunathan
Published in The Journal of chemical physics (14.04.2019)
Published in The Journal of chemical physics (14.04.2019)
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Journal Article
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
von Lilienfeld, O. Anatole, Ramakrishnan, Raghunathan, Rupp, Matthias, Knoll, Aaron
Published in International journal of quantum chemistry (15.08.2015)
Published in International journal of quantum chemistry (15.08.2015)
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Journal Article
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
Hansen, Katja, Biegler, Franziska, Ramakrishnan, Raghunathan, Pronobis, Wiktor, von Lilienfeld, O. Anatole, Müller, Klaus-Robert, Tkatchenko, Alexandre
Published in The journal of physical chemistry letters (18.06.2015)
Published in The journal of physical chemistry letters (18.06.2015)
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Journal Article
Critical benchmarking of popular composite thermochemistry models and density functional approximations on a probabilistically pruned benchmark dataset of formation enthalpies
Das, Sambit Kumar, Chakraborty, Sabyasachi, Ramakrishnan, Raghunathan
Published in The Journal of chemical physics (28.01.2021)
Published in The Journal of chemical physics (28.01.2021)
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Journal Article
The chemical space of B, N-substituted polycyclic aromatic hydrocarbons: Combinatorial enumeration and high-throughput first-principles modeling
Chakraborty, Sabyasachi, Kayastha, Prakriti, Ramakrishnan, Raghunathan
Published in The Journal of chemical physics (21.03.2019)
Published in The Journal of chemical physics (21.03.2019)
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Journal Article
Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules
Gupta, Amit, Chakraborty, Sabyasachi, Ramakrishnan, Raghunathan
Published in Machine learning: science and technology (01.09.2021)
Published in Machine learning: science and technology (01.09.2021)
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Journal Article
Fast and accurate predictions of covalent bonds in chemical space
Chang, K Y Samuel, Fias, Stijn, Ramakrishnan, Raghunathan, von Lilienfeld, O Anatole
Published in The Journal of chemical physics (07.05.2016)
Published in The Journal of chemical physics (07.05.2016)
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