Publications

Link to Google Scholar

  1. Comment on “Designing potentially singlet fission materials with an anti-Kasha behaviour” by R. Pino-Rios, R. Báez-Grez, D. W. Szczepanik, and M. Solá, Phys. Chem. Chem. Phys., 2024, 26, 15386
    Komal Jindal, Atreyee Majumdar and Raghunathan Ramakrishnan
    Phys. Chem. Chem. Phys. 27 (2025) 4968.
    Link to the article by Pino-Rios et al.
    Reply by Pino-Rios et al. to our comment
  2. Chemical Space-Informed Machine Learning Models for Rapid Predictions of X-ray Photoelectron Spectra of Organic Molecules
    Susmita Tripathy, Surajit Das, Shweta Jindal, Raghunathan Ramakrishnan
    Machine Learning: Science and Technology 5 (2024) 045023.
    Additional content on GItHub
  3. Influence of Pseudo-Jahn–Teller Activity on the Singlet-Triplet Gap of Azaphenalenes
    Atreyee Majumdar, Komal Jindal, Surajit Das, Raghunathan Ramakrishnan
    Phys. Chem. Chem. Phys. 26 (2024) 26723.
    Supplementary information
  4. Resilience of Hund’s rule in the Chemical Space of Small Organic Molecules (2024 PCCP Hot Article)
    Atreyee Majumdar, Raghunathan Ramakrishnan
    Phys. Chem. Chem. Phys. 26 (2024) 14505.
    Supplementary information
    Additional content on GitHub
  5. Structure prediction from spectra amidst dynamical heterogeneity in melanin
    Arpan Choudhury, Raghunathan Ramakrishnan, Debashree Ghosh
    Chem. Commun. 60 (2024) 2613.

  6. Stereo-Electronic Factors Influencing the Stability of Hydroperoxyalkyl Radicals: Transferability of Chemical Trends across Hydrocarbons and ab initio Methods
    (Formerly: Leveraging Stereo-Electronic Factors for ab initio Design of Long-lived Hydroperoxyalkyl Radicals)
    Saurabh Chandra Kandpal, Kgalaletso P. Otukile, Shweta Jindal, Salini Senthil, Cameron Matthews, Sabyasachi Chakraborty, Lyudmila V. Moskaleva, Raghunathan Ramakrishnan
    Physical Chemistry Chemical Physics, 25 (2023) 27302.
    Supplementary information
    Details of W1 calculations

  7. Band gaps of long-period polytypes of IV, IV-IV, and III-V semiconductors estimated with an Ising-type additivity model
    Raghunathan Ramakrishnan, Shruti Jain
    The Journal of Chemical Physics, 159 (2023) 124702.
    JCP Special Topic: John Perdew Festschrift
    Supplementary material
    Additional content on GitHub

  8. Understanding the role of intramolecular ion-pair interactions in conformational stability using an ab initio thermodynamic cycle
    Sabyasachi Chakraborty, Kalyaneswar Mandal, Raghunathan Ramakrishnan
    Journal of Physical Chemistry B, 127 (2023) 648.
    Supplementary Information: Raw I/O files, Jupyter notebooks

  9. The Resolution-vs.-Accuracy Dilemma in Machine Learning Modeling of Electronic Excitation Spectra
    Prakriti Kayastha, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
    Digital Discovery, 1 (2022) 689-702.
    bigQM7w dataset
    Raw input/output files on NOMAD
    Data-mining platform on MolDis
    Machine learning model

  10. Data-Driven Modeling of S0 -> S1 Excitation Energy in the BODIPY Chemical Space: High-Throughput Computation, Quantum Machine Learning, and Inverse Design
    Amit Gupta, Sabyasachi Chakraborty, Debashree Ghosh, Raghunathan Ramakrishnan
    The Journal of Chemical Physics, 155 (2021) 244102.
    BODIPYs dataset
    Web-based QML model for querying on 253 Billion BODIPY molecules
    JCP Special Topic: Chemical Design by Artificial Intelligence

  11. Machine Learning Modeling of Materials with a Group-Subgroup Structure
    Prakriti Kayastha, Raghunathan Ramakrishnan
    Machine Learning: Science and Technology, 2 (2021) 035035.
    FriezeRMQ1D dataset
    Raw input/output files on NOMAD

  12. Troubleshooting Unstable Molecules in Chemical Space
    Salini Senthil, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
    Chemical Science 12 (2021) 5566.
    Features in 2021 Chemical Science Editor’s Choice
    Supplementary Information: PDF file
    ConnGO code
    Curated QM9 dataset

  13. Revving up 13C NMR shielding predictions across chemical space: Benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules
    Amit Gupta, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
    Machine Learning: Science and Technology, 2 (2021) 035010.
    Supplementary Information: PDF file

  14. High-Throughput Design of Peierls and Charge Density Wave Phases in Q1D Organometallic Materials
    Prakriti Kayastha, Raghunathan Ramakrishnan
    The Journal of Chemical Physics, 154 (2021) 061102.
    Supplementary Information: PDF file
    MolDis data-mining platform
    Raw input/output files on NOMAD
    JCP Special Topic: Computational Materials Discovery

  15. Critical Benchmarking of the G4(MP2) Model, the Correlation Consistent Composite Approach and Popular Density Functional Approximations on a Probabilistically Pruned Benchmark Dataset of Formation Enthalpies
    Sambit Kumar Das, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
    The Journal of Chemical Physics, 154 (2021) 044113.
    prunedHOF dataset

  16. Quantum Interference in Real-Time Electron-Dynamics: Gaining Insights from Time-Dependent Configuration Interaction Simulations
    Raghunathan Ramakrishnan
    The Journal of Chemical Physics, 152 (2020) 194111.

  17. Quantum-chemistry-aided identification, synthesis and experimental validation of model systems for conformationally controlled reaction studies: separation of the conformers of 2,3-dibromobuta-1,3-diene in the gas phase
    Ardita Kilaj, Hong Gao, Diana Nikolaeva Tahchieva, Raghunathan Ramakrishnan, Daniel G Bachmann, Dennis Gillingham, Anatole von Lilienfeld, Jochen Küpper, Stefan Willitsch
    Physical Chemistry Chemical Physics, 22 (2020) 13431-13439.

  18. Machine learning modeling of Wigner intracule functionals for two electrons in one dimension
    Rutvij Vihang Bhavsar, Raghunathan Ramakrishnan
    The Journal of Chemical Physics,150 (2019) 144114.

  19. The Chemical Space of B,N-substituted Polycyclic Aromatic Hydrocarbons: Combinatorial Enumeration and High-Throughput First-Principles Modeling
    Sabyasachi Chakraborty, Prakriti Kayastha, Raghunathan Ramakrishnan
    The Journal of Chemical Physics,150 (2019) 114106. JCP Featured Article
    Features in 2019 JCP Editors’ Choice
    BNPAH dataset

  20. Exact separation of radial and angular correlation energies in two-electron atoms
    Anjana R Kammath, Raghunathan Ramakrishnan
    Chemical Physics Letters, 720 (2019) 93–96.

  21. Torsional potentials of glyoxal, oxalyl halides and their thiocarbonyl derivatives: Challenges for DFT
    Diana Tahchieva, Dirk Bakowies, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
    Journal of Chemical Theory and Computation, 14 (2018) 4806-4817.

  22. Generalized DFTB repulsive potentials from unsupervised machine learning
    J. J. Kranz, M. Kubillus, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld, M. Elstner
    Journal of Chemical Theory and Computation, 14 (2018) 2341-2352.

  23. Genetic optimization of training sets for improved machine learning models of molecular properties
    Nicholas J. Browning, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld, Ursula Röthlisberger
    Journal of Physical Chemistry Letters, 8 (2017) 1351-1359.

  24. Machine learning, quantum mechanics, chemical compound space
    Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
    Reviews in Computational Chemistry, Vol.30, 225-250 (2017).

  25. Fast and accurate predictions of covalent bonds in chemical space
    K. Y. Samuel Chang, Stijn Fias, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
    The Journal of Chemical Physics, 144 (2016) 174110.

  26. Electronic spectra from TDDFT and machine learning in chemical space
    Raghunathan Ramakrishnan, Mia Hartmann, Enrico Tapavicza, O. Anatole von Lilienfeld
    The Journal of Chemical Physics, 143 (2015) 084111.

  27. Machine learning for quantum mechanical properties of atoms in molecules
    Matthias Rupp, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
    Journal of Physical Chemistry Letters, 6 (2015) 3309-3313.

  28. Machine learning predictions of molecular properties: Accurate many-body potentials and non-locality in chemical space
    Katja Hansen, Franziska Biegler, Raghunathan Ramakrishnan, Wiktor Pronobis, O. Anatole von Lilienfeld, Klaus-Robert Müller, Alexandre Tkatchenko
    Journal of Physical Chemistry Letters, 6 (2015) 2326–2331.

  29. Big data meets quantum chemistry approximations: The delta-machine learning approach
    Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
    Journal of Chemical Theory and Computation, 11 (2015) 2087–2096.

  30. Semi-quartic force fields retrieved from multi-mode expansions: Accuracy, scaling behavior and approximations
    Raghunathan Ramakrishnan, Guntram Rauhut
    The Journal of Chemical Physics, 142 (2015) 154118.

  31. Many molecular properties from one kernel in chemical space
    Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
    Chimia, 69 (2015) 182-186.

  32. Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
    O. Anatole von Lilienfeld, Raghunathan Ramakrishnan, Matthias Rupp, Aaron Knoll
    International Journal of Quantum Chemistry, 115 (2015) 1084-1093.

  33. Charge transfer dynamics from adsorbates to surfaces with single active electron and configuration interaction based approaches
    Raghunathan Ramakrishnan, Mathias Nest
    Chemical Physics, 446 (2015) 24-29.

  34. Quantum chemistry structures and properties of 134 kilo molecules
    Raghunathan Ramakrishnan, Pavlo Dral, Matthias Rupp, O. Anatole von Lilienfeld
    Scientific Data 1, Article number: 140022 (2014).

  35. Vibrational energy levels of difluorodioxirane computed with variational and perturbative methods from a hybrid force field
    Raghunathan Ramakrishnan, Tucker Carrington, Jr.
    Spectrochimica Acta A, 119 (2014) 107–112.

  36. Electron dynamics across molecular wires: A time-dependent configuration interaction study
    Raghunathan Ramakrishnan, Shampa Raghunathan, Mathias Nest
    Chemical Physics, 420 (2013) 44–49.

  37. A simple Hückel molecular orbital plotter
    Raghunathan Ramakrishnan
    Journal of Chemical Education, 90 (2013) 132–133.

  38. Control and analysis of single-determinant electron dynamics
    Raghunathan Ramakrishnan, Mathias Nest
    Physics Review A, 85 (2012) 054501.

  39. Coherent control time-dependent methods for determining eigenvalues of Hermitian matrices with applications to electronic structure computations
    Raghunathan Ramakrishnan, Mathias Nest, Eli Pollak
    Molecular Physics, 110 (2012) 861–873.

  40. Self-interaction artifacts on structural features of uranyl monohydroxide from Kohn-Sham calculations
    Raghunathan Ramakrishnan, Alexei V. Matveev, Sven Krüger, Notker Rösch
    Theoretical Chemistry Accounts, 130 (2011) 361–369.

  41. Effects of the self-interaction error in Kohn-Sham calculations: A DFT + U case study on pentaaqua uranyl(VI)
    Raghunathan Ramakrishnan, Alexei V. Matveev, Notker Rösch
    Computational and Theoretical Chemistry, 963 (2011) 337–343.

  42. The DFT + U method in the linear combination of Gaussian-type orbitals framework: Role of 4f orbitals in the bonding of LuF3
    Raghunathan Ramakrishnan, Alexei V. Matveev, Notker Rösch
    Chemical Physics Letters, 468 (2009) 158–161.

  43. Manifestation of diamagnetic chemical shifts of proton NMR signals by an anisotropic shielding effect of nitrate anions
    Himansu Sekhar Sahoo, Dillip Kumar Chand, S. Mahalakshmi, Md. Hedayetullah Mir, Raghunathan Ramakrishnan
    Tetrahedron letters, 48 (2007) 761–765.

Preprints

  1. Variational augmentation of Gaussian continuum basis sets for calculating atomic higher harmonic generation spectra
    Sai Vijay Bhaskar Mocherla, Raghunathan Ramakrishnan

Software

  1. All hands on deck: Accelerating ab initio thermochemistry via wavefunction approximations
    Sambit Kumar Das, Salini Senthil, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
    Pople code