Dr. Ganesh Kumar Nayak

RWTH Aachen University, Department of Materials Chemistry

Contact

RWTH Aachen University
Chair for Materials Chemistry
Kopernikusstr. 10
52074 Aachen

materials_design@nhr4ces.de
 LinkedIn Twitter - @GaneshGakuna028

Biography

Ganesh is trained as a physicist with a Bachelor’s and Master’s degree in Physics from the Fakirmohan University, India and Central University of Punjab, India respectively. He was awarded a PhD in computational material science from Montanuniversität Leoben, Austria, working with simulation methods such as ab initio, molecular dynamics, and the fitting of interatomic potential by machine learning to simulate disorder materials.

Since January 2023,  Ganesh has been a postdoctoral researcher at Department of Materials chemistry RWTH Aachen University, Germany. He  continues his research on simulation of materials by fitting of machine learning interatomic potential and investigating the structure-property relationships in materials by machine-learning.

Thematic Advice

The atomic simulation approaches of disorder materials, e.g., grain boundaries and amorphous, require very large simulation boxes, as such, there are severe challenges for ab initio calculations. Hence, one must resort to large-scale methods such as molecular dynamics simultions to obtain properties comparable to experiments.

The accuracy of such simulations depends on the ineratomic potential that defines the system’s interactins. Ganesh can offer experience to fit and use machine learning (ML) interatomic potentials, which opens the door to studying the materials in large-scale simulations with various time and length scales.

Professional Competence

Ganesh focus lies on the simulation of materials properties from ab initio calculations to large-scale molecular dynamics when necessary, using trained a ML interatimic potentials; this includes the use of own fitted ML potentials. In the ab initio-guided materials design process, the properties are predicted by the ab initio and followed by the fitting of the ML potential using the ab initio data. Moreover, Ganesh is developing an interactive platform with the help of Python and Jupyter Notebook to enable atomistic simulations with high-performance computers for nonunix users.

In this context of the high-performance computing, the atomistic simulation with the interactive platform could attract the material scientist, has become a significant objective in addition to his collaborative works with machine learning.