Research Papers
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Das, S. & Ramakrishnan, R. (2026).
Enhancing NMR Shielding Predictions of Atoms-in-Molecules Machine Learning Models with Neighborhood-Informed Representations.
J. Chem. Phys., 164(4), 044106.
DOI: 10.1063/5.0306349
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Banchode, R., Das, S., Raghunathan, S., & Ramakrishnan, R. (2025).
Machine-Learned Potentials for Solvation Modeling.
J. Phys.: Condens. Matter, 38(1), 013002.
DOI: 10.1088/1361-648X/ae2177
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Majumdar, A., Das, S., & Ramakrishnan, R. (2025).
Unlocking Inverted Singlet-Triplet Gap in Alternant Hydrocarbons with Heteroatoms.
Chem. Sci., 16(31), 14392-14407.
DOI:10.1039/D5SC02309B
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Tripathy, S., Das, S., Jindal, S., & Ramakrishnan, R. (2024).
Chemical Space-Informed Machine Learning Models for Rapid Predictions of X-ray Photoelectron Spectra of Organic Molecules.
Mach. Learn.: Sci. Technol., 5(4), 045023.
DOI:10.1088/2632-2153/ad871d
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Majumdar, A., Jindal, K., Das, S., & Ramakrishnan, R. (2024).
Influence of pseudo-Jahn-Teller activity on the singlet-triplet gap of azaphenalenes.
Phys. Chem. Chem. Phys., 26(42), 26723-26733.
DOI:10.1039/D4CP02761B