AKHIL REDDY PEEKETI

I am a Postdoctoral Associate at Los Alamos National Laboratory (LANL), where I investigate the electronic structure and electrochemical behavior of transition-metal oxides to design next-generation electrocatalysts for fuel cells and electrolyzers. My research combines density-functional theory (DFT) with advanced modeling frameworks to understand how oxidation states, charge transfer, and metal–oxygen hybridization govern catalytic performance under realistic operating conditions.

Broadly, my work focuses on developing computational frameworks that bridge quantum, atomistic, and continuum scales to unravel and engineer complex behaviors in functional materials. I use and integrate techniques such as DFT, Molecular Dynamics (MD), Finite Element Method (FEM), and Discrete Element Method (DEM)—often coupling them with machine-learning-based surrogate models to accelerate simulations and enable data-driven materials design.

Before joining LANL, I earned my Ph.D. in Mechanical Engineering from IIT Madras, where I developed multiphysics models for light- and heat-responsive liquid-crystal polymers (LCPs) and hierarchical DEM–FEM frameworks for thermal transport in granular systems relevant to fusion and energy-storage applications. My doctoral and collaborative work with research groups in the Netherlands (TU Eindhoven, Groningen) and Germany (KIT) explored photo-thermo-mechanical coupling, molecular isomerization kinetics, and nature-inspired soft actuators, leading to publications in Advanced Materials, Applied Physics Reviews, Journal of Chemical Physics, Soft Matter, Mechanics of Materials, and ACS Applied Materials & Interfaces, among others.

Across these projects, a central theme of my research has been connecting physical mechanisms across scales—from atomic interactions to macroscopic deformation and transport—to build predictive models that not only explain but also guide material and process design. My current work extends these ideas to electrocatalytic interfaces, where understanding electronic conductivity and mixed-valence behavior is key to advancing sustainable energy technologies.

I am proficient in a wide suite of modeling and analysis tools, including VASP, Quantum ESPRESSO, LAMMPS, Materials Studio, COMSOL, Abaqus (FORTRAN subroutines), ANSYS, and MATLAB, with programming expertise in Python and Fortran.

At heart, I am driven by a simple idea: using simulations not just to describe materials — but to engineer them, accelerating discovery at the intersection of physics, chemistry, and mechanics.