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  1. A Chemical Space Perspective on Diastereomeric Barriers in Alkylperoxy-to-Hydroperoxyalkyl Isomerization (Stephen J. Klippenstein Festschrift VSI Virtual Special Issue.) Raghunathan Ramakrishnan Journal of Physical Chemistry A, (2026) in press. SEARS dataset: Stereochemically Expanded Autooxidation Reaction Space dataset

  2. Indistinguishability domains of neural microcircuit motifs mapped through classification scores of postsynaptic spike counts Anjali Naveen Kumar, Raghunathan Ramakrishnan CODS ’25: Proceedings of the 13th ACM IKDD International Conference on Data Science (2026)

  3. Insights into Symmetry and Substitution Patterns Governing Singlet-Triplet Energy Gap in the Chemical Space of Azaphenalenes Atreyee Majumdar, Raghunathan Ramakrishnan Chemistry, A European Journal (2026). AP117 Dataset: https://doi.org/10.5281/zenodo.17567791

  4. Enhancing NMR Shielding Predictions of Atoms-in-Molecules Machine Learning Models with Neighborhood-Informed Representations Surajit Das, Raghunathan Ramakrishnan The Journal of Chemical Physics (2026). Dataset: https://github.com/moldis-group/mlqm9nmr

  5. Machine-learned potentials for solvation modeling Roopshree Banchode, Surajit Das, Shampa Raghunathan, Raghunathan Ramakrishnan Journal of Physics: Condensed Matter, 38, 013002 (2026). Dataset: https://github.com/raghurama123/Rev-MLP4Sol

  6. Leveraging the Bias‐Variance Tradeoff in Quantum Chemistry for Accurate Negative Singlet‐Triplet Gap Predictions: A Case for Double‐Hybrid DFT Atreyee Majumdar, Raghunathan Ramakrishnan Journal of Computational Chemistry  46 (2025) e70228. Supplementary information: PDF Dataset: https://github.com/moldis-group/triangulenes12

  7. Unlocking inverted singlet–triplet gap in alternant hydrocarbons with heteroatoms Atreyee Majumdar, Surajit Das, Raghunathan Ramakrishnan Chemical Science  16 (2025) 14392. Supplementary information: PDF Dataset: https://github.com/moldis-group/DFIST-BNPAH

  8. 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

  9. 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

  10. 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

  11. 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

  12. Structure prediction from spectra amidst dynamical heterogeneity in melanin Arpan Choudhury, Raghunathan Ramakrishnan, Debashree Ghosh Chem. Commun. 60 (2024) 2613.

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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.

  24. 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.

  25. 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.

  26. 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

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

  28. 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.

  29. 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.

  30. 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.

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

  32. 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.

  33. 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.

  34. 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.

  35. 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.

  36. 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.

  37. 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.

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

  39. 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.

  40. 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.

  41. 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).

  42. 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.

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

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

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

  46. 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.

  47. 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.

  48. 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.

  49. 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.

  50. 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

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