Ramakrishnan, Raghunathan

1. Revving up 13C NMR shielding predictions across chemical space:\n Benchmarks for atoms-in-molecules kernel machine learning with new data for\n 134 kilo molecules
   2020/09/14 by Amit Gupta, Sabyasachi Chakraborty, Gupta, Amit +3 · 3 citations
   Materials Science · Computer Science · Biochemistry, Genetics and Molecular Biology · #Machine Learning in Materials Science #Computational Drug Discovery Methods #Metabolomics and Mass Spectrometry Studies
2. Many Molecular Properties from One Kernel in Chemical Space
   2015/01/01 by Raghunathan Ramakrishnan, O. Anatole von Lilienfeld, Ramakrishnan, Raghunathan +1 · 2 citations
   Chemistry · Computer Science · Materials Science · #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #FOS: Physical sciences #Machine Learning in Materials Science #Various Chemistry Research Topics
3. Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
   2013/07/10 by O. Anatole von Lilienfeld, Raghunathan Ramakrishnan, von Lilienfeld, O. Anatole +5 · 1 citation
   Chemistry · Computer Science · Materials Science · #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #Crystallography and molecular interactions #FOS: Physical sciences #Machine Learning in Materials Science
4. Resolution-vs.-Accuracy Dilemma in Machine Learning Modeling of Electronic Excitation Spectra
   2021/10/22 by Kayastha, Prakriti, Chakraborty, Sabyasachi, Ramakrishnan, Raghunathan · 1 citation
   #Chemical Physics (physics.chem-ph) #FOS: Physical sciences
5. Machine-Learned Potentials for Solvation Modeling
   2025/05/28 by Banchode, Roopshree, Das, Surajit, Raghunathan, Shampa +1 · 1 citation
   #Chemical Physics (physics.chem-ph) #FOS: Physical sciences