Sampreeti Bhattacharya

I am a theoretical chemist specializing in the development and application of computational methods to explore the atomic-scale behavior of molecular and condensed matter systems. My work integrates density functional theory (DFT), many-body perturbation theory, classical simulations, and physics-informed machine learning to uncover fundamental insights into complex physical and chemical phenomena. By combining accurate electronic structure techniques with scalable classical models and data-driven approaches, my research aims to both predict and design novel materials and molecular systems. A key focus of my work is to bridge the gap between theory and experiment, ensuring that computational insights are both grounded in physical principles and relevant to real-world applications in chemistry, materials science, and nanotechnology.

Publications

• Gloor, C., Gan, Z., Bhattacharya, S., You, W., Shanahan, J., Hearne, W., Chen, C.-H., Hu, J., Liang, Y., Kanai, Y., & Moran, A. "Spacer Cation Electronic Structure Controls Excited-State Pathways in Two-Dimensional Hybrid Perovskites (2026)" Link 

• Bhattacharya, S., Xu, J., Zhou, R., & Kanai, Y. "Proton Quantum Effects on Electronic Excitation in Hydrogen-Bonded Organic Solid: A First-Principles Green’s Function Theory Study." The Journal of Physical Chemistry Letters 17.5, (2026): 1419-1427 Link 

• Bhattacharya, S., Li, J., Yang, W., & Kanai, Y "BSE@ GW Prediction of Charge Transfer Exciton in Molecular Complexes: Assessment of Self-Energy and Exchange-Correlation Dependence." The Journal of Physical Chemistry A 128.29 (2024): 6072-6083 Link 

• Bhattacharya, S., & Kanai, Y  "Spin-orbit-coupling-induced band splitting in two-dimensional hybrid organic-inorganic perovskites: Importance of organic cations." Physical Review Materials 7.5 (2023): 055001. Link  

Preprints/Under review

My research also integrates machine learning approaches to revolutionize the study and prediction of material properties. By combining data-driven algorithms with first-principles methods, I aim to enhance the understanding of electronic, structural, and dynamic behaviors in complex materials. Utilizing techniques like neural networks and advanced regression models, I work on developing predictive frameworks that can efficiently map the relationships between atomic structures and their macroscopic properties. This approach accelerates the discovery of new materials with tailored functionalities for energy storage, catalysis, and electronics. Ultimately, the goal is to bridge theoretical insights and experimental outcomes, enabling innovative solutions in material design and sustainable technological applications.

Publications

• Bhattacharya, S., & Roy, A "Linking stability with molecular geometries of perovskites and lanthanide richness using machine learning methods." Computational Materials Science 231 (2024): 112581. Link 

• Bhattacharya, S., Thomas, A. J., & Kanai, Y.. "Predicting Dynamic Spin Splitting in 2D Hybrid Organic-Inorganic Perovskites via Machine Learning Model"  Chemical Physics Reviews (2025) for the special issue on AI and Machine-Learning in Chem and Mat. Sci. Link 

• Bhattacharya, S., & Roy, A.  "How Molecular Weight Shapes the Relative Importance of Chemical and Structural Descriptors for Aqueous Solubility-Preprint"