Dr. Sudheer Ganisetti

Generated machine learning force-fields (MLFF) for alkali silicate glasses. Studied composition-structure property correlations using MLFF as part of ERC-funded "NewGLASS" project.

Successfully generated DFT data to prepare machine learning force-fields Actively implementing machine learning algorithms for fitting force-fields Validating the machine-learned force-fields by computing various properties of oxide glasses Computing NMR parameters for oxide glasses Successfully generated MD force-fields for Pd-nano particles using classical fitting methods

Designed and executed computer experiments; Implemented potential model in a software called IMD; Developed new characterization methods for analyzing glass structures from the simulation data; Written several codes in Python, C, C++, bash, awk and Fortran programming languages; Extensive data analysis; Supervised master student for a mini project; Administered visualisation workstation; Prepared and presented scientific talks in group meetings and international conferences;

Conducted research activities by studying diffusion path of carbon in steel using DFT and FEM simulations

Taught physics to bachelor students; Supervised laboratory experiments; Prepared study materials for bachelor students;

Atomistic Simulations of Glass Materials

Multiscale Modelling of Materials; Density Functional Theory; Phase Field; Finite Element Method

Computational Physics; Condensed Matter Physics; Quantum Mechanics; Classical Mechanics; Electronics; Mathematical Physics

Physics; Mathematics; Chemistry