Hi there! I am Sabyasachi. I am a computational chemist. I am interested in investigating chemical spaces using quantum chemistry. Since chemical spaces are large, my colleagues and I identify and study relatively small representative subsets of these spaces. We hope to identify transferable chemical trends that may help us discover functional molecules.

Presently, I am working as a postdoc with Dr. Renana Gershoni-Poranne at the Schulich Faculty of Chemistry in Technion, Israel Institute of Technology. We are trying to understand how heteroatomic substitutions influence structural/electronic properties in polycyclic aromatic hydrocarbons through the prism of aromaticity.

I did my Ph. D. under Dr. Raghunathan Ramakrishnan's supervision at TIFR-Hyderabad, India. In my thesis titled 'Quantum chemical explorations across diverse chemical spaces' we generated, curated and studied the:

B, N -substituted polycyclic aromatic hydrocarbons

QM9 ( ¹³C NMR/curated QM9)

BODIPY

PPE1694

chemical spaces/datasets using quantum chemistry and machine learning. We identified diverse chemical trends and made most data publicly accessible via MolDis for data-mining applications. We also devised a novel thermodynamic scheme to investigate ion-pair interactions in the fascinating biomolecular chemical space.

if (you want to discuss science):

please feel free to reach out any time through my socials.

elif (you are an editor/author):

I am available to review your manuscript.
Email ID: sabyasachi@campus.technion.ac.il

else:

Live long and prosper!

COMPAS-4: A Data Set of (BN)1 Substituted Cata-Condensed Polybenzenoid Hydrocarbons─Data Analysis and Feature Engineering
Sabyasachi Chakraborty, Itay Almog, and Renana Gershoni-Poranne
Journal of Chemical Information and Modeling (2025)

How local is ``local”? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons
Yair Davidson, Aviad Philipp, Sabyasachi Chakraborty, Alex M. Bronstein, and Renana Gershoni-Poranne
The Journal of Chemical Physics, 162 (14), 144101 (2025)

Polybenzenoid Hydrocarbons in the S1 State: Simple Structural Motifs Predict Electronic Properties and (Anti)Aromaticity
Fatimah Khaleel, Sabyasachi Chakraborty, and Renana Gershoni-Poranne
Journal of Physical Organic Chemistry, 38 (5), e70012 (2025)

Hetero-polycyclic aromatic systems: A data-driven investigation of structure-property relationships
Sabyasachi Chakraborty, Eduardo Mayo Yanes, and Renana Gershoni-Poranne
Beilstein Journal of Organic Chemistry, 20, 1817 (2024)

COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems.
Eduardo Mayo Yanes, Sabyasachi Chakraborty, and Renana Gershoni-Poranne
Scientific Data, 11, 97 (2024)

Stereo-electronic factors influencing the stability of hydroperoxyalkyl radicals: transferability of chemical trends across hydrocarbons and ab initio methods
Saurabh Chandra Kandpal, Kgalaletso P. Otukile, Shweta Jindal, Salini Senthil, Cameron Matthews, Sabyasachi Chakraborty, Lyudmila V. Moskaleva, and Raghunathan Ramakrishnan
Physical Chemistry Chemical Physics, 25, 27302 (2023)

Guided Diffusion for Inverse Molecular Design.
Tomer Weiss, Eduardo Mayo Yanes, Sabyasachi Chakraborty, Luca Cosmo, Alex M. Bronstein, and Renana Gershoni-Poranne
Nature Computational Science, 3, 10, 873, (2023)

Understanding the role of intramolecular ion-pair interactions in conformational stability using an ab initio thermodynamic cycle.
Sabyasachi Chakraborty, Kalyaneswar Mandal, and Raghunathan Ramakrishnan
The Journal of physical chemistry B 127, 3, 648 (2023)

The resolution-vs.-accuracy dilemma in machine learning modeling of electronic excitation spectra.
Prakriti Kayastha, Sabyasachi Chakraborty, and Raghunathan Ramakrishnan
Digital Discovery 1, 689 (2022)

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, and Raghunathan Ramakrishnan
The Journal of chemical physics 155 (24), 244102 (2021)

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, and Raghunathan Ramakrishnan
Machine Learning: Science and Technology 2 (3), 035010 (2021)

All Hands on Deck: Accelerating Ab Initio Thermochemistry via Wavefunction Approximations.
Sambit Kumar Das, Salini Senthil, Sabyasachi Chakraborty, and Raghunathan Ramakrishnan
ChemRxiv: 10.26434/chemrxiv.14524890.v1 (2021)

Critical benchmarking of popular composite thermochemistry models and density functional approximations on a probabilistically pruned benchmark dataset of formation enthalpies.
Sambit Kumar Das, Sabyasachi Chakraborty, and Raghunathan Ramakrishnan
The Journal of chemical physics 154 (4), 044113 (2021)

Troubleshooting unstable molecules in chemical space.
Salini Senthil, Sabyasachi Chakraborty, and Raghunathan Ramakrishnan
Chemical science 12 (15), 5566 (2021)

The chemical space of B, N-substituted polycyclic aromatic hydrocarbons: Combinatorial enumeration and high-throughput first-principles modeling.
Sabyasachi Chakraborty, Prakriti Kayastha, and Raghunathan Ramakrishnan
The Journal of chemical physics 150 (11), 114106 (2019), Featured Article, Editor's Pick

"Effect of B, N substitution on polybenzenoid hydrocarbons: A computational investigation" at TPOC 2024 (March 14)

"The Effect of B, N Substitution on Polybenzenoid Hydrocarbons: A Computational Investigation" at ICESAA3, Dubrovnik, Croatia, 2024 (July 9)

"The Ground and Excited States of Hetero-Heptalenes: a Computational Investigation" at Schulich Faculty Day, Technion, Israel, 2025 (February 24)

Presented "7,453,041,547,842 BN-PAH molecules" at Pan-TIFR Chemistry Meet-2018, and SDMC-2019, Shimla, India.

Presented "A thermodynamic cycle of intramolecular ion-pair interactions" at TACC-2019, Mumbai, NSRAC-2020, Pondicherry, and SDMC-2020, Udaipur.