Kanchan Sarkar got his formal training in soft computing and machine intelligence methods from Center for Soft Computing Research and Machine Intelligence Unit, Indian Statistical Institute - Kolkata. During doctoral training in Indian Association for the Cultivation of Science - Kolkata, he experimented with several pure and hybrid soft-computing techniques in the general context of computing minimum energy structures of atomic, molecular and ionic clusters, undoped and bipolaron-doped oligomers. Currently, he is a postdoctoral associate in the Department of Chemical Engineering and Materials Science, University of Minnesota. His postgraduate studies focus on the enormous possibilities that the techniques of evolutionary computing can offer a consistent procedure of generating projector augmented wave (PAW) data-sets maintaining the same level of accuracy as all-electron full potential linearized augmented plane wave (AE-FLAPW) calculations up to high pressures. He has also introduced a new measure of atomic data-set quality by considering performance uniformity over an extended pressure range. His other ongoing projects involve computing ab initio thermoelastic properties of materials under extremely high temperature and pressure conditions, and designing atomic data-sets for lower mantle conditions to understand deep-earth processes. He is also one of the active software developer for the Virtual Laboratory of Earth and Planetary Materials (VLab) for high temperature and high-pressure elasticity calculations.
Post-Doctorate, APAM, Columbia University, NYC, 2017-
Post-Doctorate, CEMS, University of Minnesota, 2014-2017
Research Scientist-I, IACS, Kolkata, 2014
PhD, IACS, Kolkata, 2014
Areas of Research / Professional Expertise
Designing materials with interesting experimental properties by theoretically/computationally suggesting structural motifs with predetermined properties.
Minimum Energy Path for chemical processes.
Developing multiobjective optimization algorithm for generating accurate, transferable and
efficient projector augmented wave (PAW) data-sets.
Electronic, Magnetic & Photonic Materials.
Materials Theory.