Curriculum Vitae

Surajit Das

Field of Interest: Computational NMR, Machine Learning, Aromaticity
Currently working as a Integrated Masters and PhD student (2023- ) at Tata Institute of Fundamental Research, Hyderabad (India)
Email: onlinesurajitdas@gmail.com,
    sdas@tifrh.res.in,
    surajit18@alumni.iitg.ac.in
Current Group: MolDis
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1. 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
2. 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
3. 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
4. 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
5. 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

1. Ray, P., Das, S., Goswami, D., Das, S. 'Chapter 13: Prospects of Ecosystem Services for Fly Ash'. Circular Economy and Sustainable Management. DOI:10.1201/9781003498124

1. Reoprt on Molecular Dynamics of MOF, under the guidance of Dr. Jagannath Mondal, Associate Professor, TATA Institute of Fundamental Research Hyderabad. (Graduate Student Lab Rotation, Nov-Dec 2023)
2. Review on Greener o-NosylOXY: A reagent for synthesis, under the guidance of Dr. Bhubaneswar Mandal, Professor, Department of Chemistry, Indian Institute of Technology Guwahati. (Bachelor of Technology Project-II, Jan-Apr 2022)
3. Review on Mechanism and Application of Dakin West Reaction, under the guidance of Dr. Bhubaneswar Mandal, Professor, Department of Chemistry, Indian Institute of Technology Guwahati. (Bachelor of Technology Project-I, Jul-Nov 2021)

• Programming Language: Python, Fortran 90/95
• Markup Language: HTML
• Software: Gaussian, ORCA, CFOUR, VASP, MOPAC

• RSCposter2026, online conference orgainzed by RSC hosted on Linkedin. (Poster: Enhancing NMR Shielding Predictions of Atoms-in-Molecules Machine Learning Models with Neighborhood-Informed Representations.)
• CODS 2025 held at IISER Pune.
• TCS 2025 held at IIT Bombay. (Poster: Enhancing NMR Shielding Predictions of Atoms-in-Molecules Machine Learning Models with Neighborhood-Informed Representations.)
• TACC 2025 held at TIFR Mumbai. (Poster: Enhancing NMR Shielding Predictions of Atoms-in-Molecules Machine Learning Models with Neighborhood-Informed Representations.)
• In-house Symposium 2025 held at TIFR Hyderabad. (Poster: Enhancing NMR Shielding Predictions of Atoms-in-Molecules Machine Learning Models with Neighborhood-Informed Representations.)
• AIBiophysics 2025 held at IISc Bangaluru. (Poster: Discrete and Continuous Representations for Atoms-in-Molecules Machine Learning Inspired by Many-Body Interaction.)
• RSCposter2025, online conference orgainzed by RSC hosted on Linkedin. (Poster: Discrete and Continuous Representations for Atoms-in-Molecules Machine Learning: Pros and Cons.)
• ML4Science 2025 held at Shillong, India. (Poster: Discrete and Continuous Representations for Atoms-in-Molecules Machine Learning: Pros and Cons.)
• ChemSci 2024: Leaders in the field symposium organized by RSC held in IISER TVM. (Poster: Discrete and Continuous Representations for Atoms-in-Molecules Machine Learning: Pros and Cons.)
• LatinXChem24, online conference orgainzed by LatinXChem hosted on X. (Poster: Machine Learning Prediction of NMR Chemical Shifts Using Discrete Representaion.)