💻 Software & Environment Setup
Nanotechnology & Materials Science
The computational toolchain and environment used throughout Master Reinforcement Learning for Battery & Material Science.
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# Recommended conda environmentconda create -n dstc_env python=3.11 -yconda activate dstc_envpip install numpy scipy pymatgen ase matplotlibpymatgen
Materials analysis & genomics
Materials analysis & genomics
ASE
Atomic simulation environment
Atomic simulation environment
ImageJ / Fiji
SEM/TEM image quantification
SEM/TEM image quantification
OriginPro / Python
XRD & spectra analysis
XRD & spectra analysis
Supported OS: Linux (Ubuntu 22.04+), macOS, or Windows via WSL2.
Hardware: 16 GB RAM minimum; a CUDA GPU helps for deep-learning modules.
Earn government-registered certification in Master Reinforcement Learning for Battery & Material Science
e-Certificate and e-Marksheet issued on successful completion.
