{"id":28,"date":"2023-12-25T12:07:44","date_gmt":"2023-12-25T12:07:44","guid":{"rendered":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/?page_id=28"},"modified":"2026-05-09T08:41:46","modified_gmt":"2026-05-09T08:41:46","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/?page_id=28","title":{"rendered":"Publications"},"content":{"rendered":"

[et_pb_section fb_built=”1″ _builder_version=”4.23″ _module_preset=”default” custom_padding=”5px|||||” global_colors_info=”{}”][et_pb_row _builder_version=”4.23″ _module_preset=”default” border_width_bottom=”1px” border_color_bottom=”#e2e2e2″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.23″ _module_preset=”default” global_colors_info=”{}”][et_pb_post_title meta=”off” featured_image=”off” _builder_version=”4.23″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_post_title][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.23″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.23″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.23″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Link to Google Scholar<\/a><\/p>\n

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  1. Indistinguishability domains of neural microcircuit motifs mapped through classification scores of postsynaptic spike counts<\/a><\/em>
    Anjali Naveen Kumar, Raghunathan Ramakrishnan
    <\/span><\/span><\/span>CODS ’25: Proceedings of the 13th ACM IKDD International Conference on Data Science (2026)\u00a0
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  2. Insights into Symmetry and Substitution Patterns Governing Singlet-Triplet Energy Gap in the Chemical Space of Azaphenalenes<\/em><\/a>
    <\/span>Atreyee Majumdar, Raghunathan Ramakrishnan
    Chemistry, A European Journal (2026).
    AP117 Dataset:     https:\/\/doi.org\/10.5281\/zenodo.17567791<\/a><\/span>
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  3. Enhancing NMR Shielding Predictions of Atoms-in-Molecules Machine Learning Models with Neighborhood-Informed Representations<\/em><\/a>
    Surajit Das, Raghunathan Ramakrishnan
    <\/span><\/span>The Journal of Chemical Physics (2026).
    <\/span><\/span>Dataset:     https:\/\/github.com\/moldis-group\/mlqm9nmr<\/a><\/span>
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  4. Machine-learned potentials for solvation modeling
    <\/em><\/a>Roopshree Banchode, Surajit Das, Shampa Raghunathan, Raghunathan Ramakrishnan
    <\/span>Journal of Physics: Condensed Matter, 38, 013002 (2026).
    <\/span><\/span>Dataset:     https:\/\/github.com\/raghurama123\/Rev-MLP4Sol<\/a><\/span>
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  5. Leveraging the Bias\u2010Variance Tradeoff in Quantum Chemistry for Accurate Negative Singlet\u2010Triplet Gap Predictions: A Case for Double\u2010Hybrid DFT<\/span><\/a><\/em><\/span>
    Atreyee Majumdar, Raghunathan Ramakrishnan
    Journal of Computational Chemistry\u00a0 46 (2025) e70228<\/span>.
    Supplementary information: PDF<\/span><\/a>
    Dataset:     https:\/\/github.com\/moldis-group\/triangulenes12<\/a><\/span>
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  6. Unlocking inverted singlet\u2013triplet gap in alternant hydrocarbons with heteroatoms<\/em><\/a><\/span>
    Atreyee Majumdar, Surajit Das, Raghunathan Ramakrishnan
    Chemical Science\u00a0 16 (2025) 14392<\/span>.
    Supplementary information: PDF<\/span><\/a>
    Dataset:     https:\/\/github.com\/moldis-group\/DFIST-BNPAH<\/a><\/span>
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  7. Comment on \u201cDesigning potentially singlet fission materials with an anti-Kasha behaviour\u201d by R. Pino-Rios, R. B\u00e1ez-Grez, D. W. Szczepanik, and M. Sol\u00e1, Phys. Chem. Chem. Phys., 2024, 26, 15386<\/a><\/span>
    Komal Jindal, Atreyee Majumdar and Raghunathan Ramakrishnan
    Phys. Chem. Chem. Phys. 27 (2025) 4968.
    Link to the article by Pino-Rios et al.<\/a><\/span>
    Reply by Pino-Rios et al. to our comment<\/a><\/span>
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  8. Chemical Space-Informed Machine Learning Models for Rapid Predictions of X-ray Photoelectron Spectra of Organic Molecules<\/em><\/a><\/span>
    Susmita Tripathy, Surajit Das, Shweta Jindal, Raghunathan Ramakrishnan
    Machine Learning: Science and Technology 5 (2024) 045023.
    Additional content on GItHub<\/a>
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  9. In\ufb02uence of Pseudo-Jahn\u2013Teller Activity on the Singlet-Triplet Gap of Azaphenalenes<\/em><\/a><\/span>
    Atreyee Majumdar, Komal Jindal, Surajit Das, Raghunathan Ramakrishnan
    Phys. Chem. Chem. Phys. 26 (2024) 26723.
    Supplementary information<\/span><\/a>
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  10. Resilience of Hund’s rule in the Chemical Space of Small Organic Molecules <\/em><\/a>(2024 PCCP Hot Article<\/span><\/a>)
    Atreyee Majumdar, Raghunathan Ramakrishnan
    Phys. Chem. Chem. Phys. 26 (2024) 14505.
    Supplementary information<\/a><\/span>
    Additional content on GitHub<\/a><\/span>
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  11. Structure prediction from spectra amidst dynamical heterogeneity in melanin<\/em><\/a>
    Arpan Choudhury, Raghunathan Ramakrishnan, Debashree Ghosh
    Chem. Commun. 60 (2024) 2613.\n

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  12. Stereo-Electronic Factors Influencing the Stability of Hydroperoxyalkyl Radicals: Transferability of Chemical Trends across Hydrocarbons and ab initio Methods<\/em><\/a>
    (Formerly: Leveraging Stereo-Electronic Factors for ab initio Design of Long-lived Hydroperoxyalkyl Radicals)<\/em>
    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<\/a>
    Details of W1 calculations<\/a>\n

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  13. Band gaps of long-period polytypes of IV, IV-IV, and III-V semiconductors estimated with an Ising-type additivity model<\/em><\/a>
    Raghunathan Ramakrishnan, Shruti Jain
    The Journal of Chemical Physics, 159 (2023) 124702.
    JCP Special Topic:\u00a0<\/span>    John Perdew Festschrift<\/a>
    Supplementary material<\/a>
    Additional content on GitHub<\/a>\n

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  14. Understanding the role of intramolecular ion-pair interactions in conformational stability using an ab initio thermodynamic cycle<\/em><\/a>
    Sabyasachi Chakraborty, Kalyaneswar Mandal, Raghunathan Ramakrishnan
    Journal of Physical Chemistry B, 127 (2023) 648.
    Supplementary Information: Raw I\/O files, Jupyter notebooks
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  15. The Resolution-vs.-Accuracy Dilemma in Machine Learning Modeling of Electronic Excitation Spectra<\/em><\/a>
    Prakriti Kayastha, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
    Digital Discovery, 1 (2022) 689-702.
    bigQM7w dataset<\/a>
    Raw input\/output files on NOMAD<\/a>
    Data-mining platform on MolDis<\/a>
    Machine learning model
    <\/a>\n

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  16. Data-Driven Modeling of S0 -> S1 Excitation Energy in the BODIPY Chemical Space: High-Throughput Computation, Quantum Machine Learning, and Inverse Design<\/em><\/a>
    Amit Gupta, Sabyasachi Chakraborty, Debashree Ghosh, Raghunathan Ramakrishnan
    The Journal of Chemical Physics, 155 (2021) 244102.
    BODIPYs dataset<\/a>
    Web-based QML model for querying on 253 Billion BODIPY molecules<\/a>
    JCP Special Topic:\u00a0<\/span>    Chemical Design by Artificial Intelligence
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  17. Machine Learning Modeling of Materials with a Group-Subgroup Structure<\/em><\/a>
    Prakriti Kayastha, Raghunathan Ramakrishnan
    Machine Learning: Science and Technology, 2 (2021) 035035.
    FriezeRMQ1D dataset<\/a>
    Raw input\/output files on NOMAD<\/a>\n

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  18. Troubleshooting Unstable Molecules in Chemical Space<\/em><\/a>
    Salini Senthil, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
    Chemical Science 12 (2021) 5566.
    Features in\u00a0<\/span>    2021 Chemical Science Editor\u2019s Choice<\/a>
    Supplementary Information: PDF file<\/a>
    ConnGO code<\/a>
    Curated QM9 dataset<\/a>\n

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

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  20. High-Throughput Design of Peierls and Charge Density Wave Phases in Q1D Organometallic Materials<\/em><\/a>
    Prakriti Kayastha, Raghunathan Ramakrishnan
    The Journal of Chemical Physics, 154 (2021) 061102.
    Supplementary Information: PDF file<\/a>
    MolDis data-mining platform<\/a>
    Raw input\/output files on NOMAD<\/a>
    JCP Special Topic:\u00a0<\/span>    Computational Materials Discovery<\/a>\n

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  21. 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<\/em><\/a>
    Sambit Kumar Das, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
    The Journal of Chemical Physics, 154 (2021) 044113.
    prunedHOF dataset<\/a>\n

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  22. Quantum Interference in Real-Time Electron-Dynamics: Gaining Insights from Time-Dependent Configuration Interaction Simulations<\/em><\/a>
    Raghunathan Ramakrishnan
    The Journal of Chemical Physics, 152 (2020) 194111.\n

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  23. 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<\/em><\/a>
    Ardita Kilaj, Hong Gao, Diana Nikolaeva Tahchieva, Raghunathan Ramakrishnan, Daniel G Bachmann, Dennis Gillingham, Anatole von Lilienfeld, Jochen K\u00fcpper, Stefan Willitsch
    Physical Chemistry Chemical Physics, 22 (2020) 13431-13439.\n

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  24. Machine learning modeling of Wigner intracule functionals for two electrons in one dimension<\/em><\/a>
    Rutvij Vihang Bhavsar, Raghunathan Ramakrishnan
    The Journal of Chemical Physics,150 (2019) 144114.\n

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  25. The Chemical Space of B,N-substituted Polycyclic Aromatic Hydrocarbons: Combinatorial Enumeration and High-Throughput First-Principles Modeling<\/em><\/a>
    Sabyasachi Chakraborty, Prakriti Kayastha, Raghunathan Ramakrishnan
    The Journal of Chemical Physics,150 (2019) 114106. JCP Featured Article
    Features in\u00a0<\/span>    2019 JCP Editors\u2019 Choice<\/a>
    BNPAH dataset<\/a>\n

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  26. Exact separation of radial and angular correlation energies in two-electron atoms<\/em><\/a>
    Anjana R Kammath, Raghunathan Ramakrishnan
    Chemical Physics Letters, 720 (2019) 93\u201396.\n

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

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  28. Generalized DFTB repulsive potentials from unsupervised machine learning<\/em><\/a>
    J. J. Kranz, M. Kubillus, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld, M. Elstner
    Journal of Chemical Theory and Computation, 14 (2018) 2341-2352.\n

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  29. Genetic optimization of training sets for improved machine learning models of molecular properties<\/em><\/a>
    Nicholas J. Browning, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld, Ursula R\u00f6thlisberger
    Journal of Physical Chemistry Letters, 8 (2017) 1351-1359.\n

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  30. Machine learning, quantum mechanics, chemical compound space<\/em><\/a>
    Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
    Reviews in Computational Chemistry, Vol.30, 225-250 (2017).\n

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  31. Fast and accurate predictions of covalent bonds in chemical space<\/em><\/a>
    K. Y. Samuel Chang, Stijn Fias, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
    The Journal of Chemical Physics, 144 (2016) 174110.\n

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  32. Electronic spectra from TDDFT and machine learning in chemical space<\/em><\/a>
    Raghunathan Ramakrishnan, Mia Hartmann, Enrico Tapavicza, O. Anatole von Lilienfeld
    The Journal of Chemical Physics, 143 (2015) 084111.\n

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  33. Machine learning for quantum mechanical properties of atoms in molecules<\/em><\/a>
    Matthias Rupp, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
    Journal of Physical Chemistry Letters, 6 (2015) 3309-3313.\n

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  34. Machine learning predictions of molecular properties: Accurate many-body potentials and non-locality in chemical space<\/em><\/a>
    Katja Hansen, Franziska Biegler, Raghunathan Ramakrishnan, Wiktor Pronobis, O. Anatole von Lilienfeld, Klaus-Robert M\u00fcller, Alexandre Tkatchenko
    Journal of Physical Chemistry Letters, 6 (2015) 2326\u20132331.\n

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  35. Big data meets quantum chemistry approximations: The delta-machine learning approach<\/em><\/a>
    Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
    Journal of Chemical Theory and Computation, 11 (2015) 2087\u20132096.\n

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  36. Semi-quartic force fields retrieved from multi-mode expansions: Accuracy, scaling behavior and approximations<\/em><\/a>
    Raghunathan Ramakrishnan, Guntram Rauhut
    The Journal of Chemical Physics, 142 (2015) 154118.\n

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  37. Many molecular properties from one kernel in chemical space<\/em><\/a>
    Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
    Chimia, 69 (2015) 182-186.\n

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

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  39. Charge transfer dynamics from adsorbates to surfaces with single active electron and configuration interaction based approaches<\/em><\/a>
    Raghunathan Ramakrishnan, Mathias Nest
    Chemical Physics, 446 (2015) 24-29.\n

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  40. Quantum chemistry structures and properties of 134 kilo molecules<\/em><\/a>
    Raghunathan Ramakrishnan, Pavlo Dral, Matthias Rupp, O. Anatole von Lilienfeld
    Scientific Data 1, Article number: 140022 (2014).\n

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  41. Vibrational energy levels of difluorodioxirane computed with variational and perturbative methods from a hybrid force field<\/em><\/a>
    Raghunathan Ramakrishnan, Tucker Carrington, Jr.
    Spectrochimica Acta A, 119 (2014) 107\u2013112.\n

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  42. Electron dynamics across molecular wires: A time-dependent configuration interaction study<\/em><\/a>
    Raghunathan Ramakrishnan, Shampa Raghunathan, Mathias Nest
    Chemical Physics, 420 (2013) 44\u201349.\n

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  43. A simple H\u00fcckel molecular orbital plotter<\/em><\/a>
    Raghunathan Ramakrishnan
    Journal of Chemical Education, 90 (2013) 132\u2013133.\n

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  44. Control and analysis of single-determinant electron dynamics<\/em><\/a>
    Raghunathan Ramakrishnan, Mathias Nest
    Physics Review A, 85 (2012) 054501.\n

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  45. Coherent control time-dependent methods for determining eigenvalues of Hermitian matrices with applications to electronic structure computations<\/em><\/a>
    Raghunathan Ramakrishnan, Mathias Nest, Eli Pollak
    Molecular Physics, 110 (2012) 861\u2013873.\n

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  46. Self-interaction artifacts on structural features of uranyl monohydroxide from Kohn-Sham calculations<\/em><\/a>
    Raghunathan Ramakrishnan, Alexei V. Matveev, Sven Kr\u00fcger, Notker R\u00f6sch
    Theoretical Chemistry Accounts, 130 (2011) 361\u2013369.\n

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  47. Effects of the self-interaction error in Kohn-Sham calculations: A DFT + U case study on pentaaqua uranyl(VI)<\/em><\/a>
    Raghunathan Ramakrishnan, Alexei V. Matveev, Notker R\u00f6sch
    Computational and Theoretical Chemistry, 963 (2011) 337\u2013343.\n

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  48. The DFT + U method in the linear combination of Gaussian-type orbitals framework: Role of 4f orbitals in the bonding of LuF3<\/em><\/a>
    Raghunathan Ramakrishnan, Alexei V. Matveev, Notker R\u00f6sch
    Chemical Physics Letters, 468 (2009) 158\u2013161.\n

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  49. Manifestation of diamagnetic chemical shifts of proton NMR signals by an anisotropic shielding effect of nitrate anions<\/em><\/a>
    Himansu Sekhar Sahoo, Dillip Kumar Chand, S. Mahalakshmi, Md. Hedayetullah Mir, Raghunathan Ramakrishnan
    Tetrahedron letters, 48 (2007) 761\u2013765.\n

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    Preprints<\/h4>\n
      \n
    1. Variational augmentation of Gaussian continuum basis sets for calculating atomic higher harmonic generation spectra<\/em><\/a>
      Sai Vijay Bhaskar Mocherla, Raghunathan Ramakrishnan\n

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      Software<\/h4>\n
        \n
      1. All hands on deck: Accelerating ab initio thermochemistry via wavefunction approximations<\/em><\/a>
        Sambit Kumar Das, Salini Senthil, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
        Pople code
        <\/a>\n

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        [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

        Link to Google Scholar Indistinguishability domains of neural microcircuit motifs mapped through classification scores of postsynaptic spike countsAnjali Naveen Kumar, Raghunathan RamakrishnanCODS ’25: Proceedings of the 13th ACM IKDD International Conference on Data Science (2026)\u00a0 Insights into Symmetry and Substitution Patterns Governing Singlet-Triplet Energy Gap in the Chemical Space of AzaphenalenesAtreyee Majumdar, Raghunathan RamakrishnanChemistry, A […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-28","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=\/wp\/v2\/pages\/28","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=28"}],"version-history":[{"count":28,"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=\/wp\/v2\/pages\/28\/revisions"}],"predecessor-version":[{"id":295,"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=\/wp\/v2\/pages\/28\/revisions\/295"}],"wp:attachment":[{"href":"https:\/\/www.tifrh.res.in\/~ramakrishnan\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=28"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}