Bikram Kumar Das

I am a Juan de la Cierva postdoctoral research fellow at BCAM, specializing in Modelling and Simulation in Life and Material Sciences. My expertise lies in Computational Physics, with over nine years of experience applying first-principles density functional theory (DFT) to explore the properties and applications of nanoscale materials.

I earned my Ph.D. in Physics from Jadavpur University, India (2022), under the supervision of Prof. Kalyan Kumar Chattopadhyay. My doctoral research focused on developing efficient carbon-based cathode materials for the oxygen reduction reaction (ORR) in fuel cells using ab-initio calculations. Through free energy analysis, electronic property studies, and descriptor identification, I isolated key surface electronic structures in graphyne- and graphdiyne-based electrocatalysts, uncovering ways to enhance their electrocatalytic activity.

Throughout my research journey, I have actively collaborated with experimentalists and theoretical researchers, contributing to projects on ab-initio molecular dynamics, lattice thermal conductivity, energy harvesting, environmental remediation, alloy structures, and photocatalysis. Additionally, I have experience in setting up and maintaining high-performance computing clusters for computational research.

Since joining BCAM in April 2022, I have been working under the supervision of Prof. Elena Akhmatskaya and Dr. Mauricio R. Bonilla on two key projects:

🔹 Decomposition of Dimethyl Carbonate (DMC) on Functionalized Graphite Surfaces
Using DFT and ab-initio molecular dynamics, I investigate the formation and composition of the solid electrolyte interface (SEI) in battery systems, focusing on the role of surface functional groups.

🔹 Hydrogen Diffusion in TRIP/TWIP Steel
Combining DFT and kinetic Monte Carlo simulations, I study the diffusion of atomic hydrogen in low manganese, low aluminum steel, exploring the impact of adatoms and impurities on hydrogen embrittlement.

I am passionate about advancing materials research through computational methods and fostering interdisciplinary collaborations.