PhD research project (Ongoing)
My PhD research focuses on finding new descriptors for material property predictions based on bonding indicators in solid state materials. This invovles combination of high-throughput DFT simulations, bonding analysis and applying machine learning to uncover relationships between bonding indicators and material properties.
Other Simulation and ML projects
I have worked on several different projects during the course of my master studies and below is the list of notable projects :
- Evaluation of tight binding recursion coefficients as candidate feature set for learning material properties by means of symbolic-regression
- Prediction of van der Waals constants for polar, non polar, hydrocarbon fluids in gas phasefluids using DFT calculated properties as candidate features
- Atomistic and Coarse grained molecular dynamics simulation of polymers to investigate influence of interaction with ions
- Evaluation of 2D materials sutiabitliy for photocatalysis application via DFT simulations
Programming projects
I regularly make contributions to the following open-source packages
- Maintaining and reviewing PRs of autoplex package (Code for automated fitting of machine learned interatomic potentials)
- Documentation and extending functionalities of Lobsterpy package (Package to automatically analyze LOBSTER runs)
- Extended functionalities of pymatgen package mainly concerning bonding (Lobster) and electronic structure analysis
- Contributed to atomate and atomate2 in computational material science workflow development (LOBSTER and Gruneisen)
- Contributed to curating tutorials for LOBSTER workflows, Automating tasks for computational materials science via Jobflow and Phonon workflow
Developed atomic-features package and wrote a tutorial on its usage for NOMAD lab
Other misc projects include
- Automated Z-scan experiments data acquistion from Lockin amplifier by controlling servo motor movements through MATLAB code (experimetal time reduced by 70%)
- Wrote a webscraper for data collection of experimental values from NIST chemistry webbook