mat-dft-electronic-transport
OfficialCompute transport properties from DFT
Authorlearningmatter-mit
Version1.0.0
Installs0
System Documentation
What problem does it solve?
It computes electronic transport properties such as carrier mobility, electrical conductivity, and the Seebeck coefficient from first principles without relying on pre-fitted empirical transport models.
Core Features & Use Cases
- First-principles transport via DFT + AMSET: couples dense band-structure information with scattering/transport calculations using AMSET integrated into an atomate2 VASP workflow.
- Automated multi-stage workflow orchestration: builds a DAG that includes structure relaxation, dense uniform band structure extraction, elastic tensor evaluation, deformation potential calculations, and final AMSET execution.
- Doping- and temperature-resolved outputs: generates transport parameters across specified doping concentrations and temperatures for comparison and screening (e.g., GaAs use case provided).
Quick Start
Generate the transport workflow DAG for your structure by running: python .agents/skills/mat-dft-electronic-transport/scripts/generate_inputs.py --output amset_flow.json.
Dependency Matrix
Required Modules
pymatgenatomate2jobflowamsetjobflow_remote
Components
scriptsreferences
💻 Claude Code Installation
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Please help me install this Skill: Name: mat-dft-electronic-transport Download link: https://github.com/learningmatter-mit/AtomisticSkills/archive/main.zip#mat-dft-electronic-transport Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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