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DSTC-01063 Online (e-LMS) Graduate / Intermediate

Density Functional Theory Modeling of MXene Heterostructures for EV Battery Materials

by - DSTC

Master Density Functional Theory Modeling of MXene Heterostructures for EV Battery Materials in 4 weeks through hands-on, project-based online training with DSTC.

★★★★★ Be the first to review 3 Days · 4.5 hrs e-Certificate Included
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From ₹2,500 + GST

💻 Software & Environment Setup

Nanotechnology & Materials Science

The computational toolchain and environment used throughout Density Functional Theory Modeling of MXene Heterostructures for EV Battery Materials.

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# Recommended conda environment
conda create -n dstc_env python=3.11 -y
conda activate dstc_env
pip install numpy scipy pymatgen ase matplotlib
pymatgen
Materials analysis & genomics
ASE
Atomic simulation environment
ImageJ / Fiji
SEM/TEM image quantification
OriginPro / Python
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 Density Functional Theory Modeling of MXene Heterostructures for EV Battery Materials

e-Certificate and e-Marksheet issued on successful completion.

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Scholar Registration

For scholars whose department, college or employer pays the fee. We raise a proforma invoice to your institution; you attach the signed processing letter or bank slip.

The proforma invoice is emailed here as well as to you.
📄 Upload Sponsorship Slip / Letter

Signed letter on official letterhead, or the bank transfer slip. PDF/JPG/PNG, up to 5 MB.

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