mat-dft-electronic-transport

Compute carrier mobility, conductivity, and Seebeck coefficient from DFT band structures using AMSET.

144|21|Updated Jan 8, 2026

Search Tags:#mobility #vasp #dft #electronic transport #amset #seebeck coefficient

Ready to run

npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill mat-dft-electronic-transport

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mat-dft-electronic-transport
Source: https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/mat-dft-electronic-transport
Command: npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill mat-dft-electronic-transport

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymatgen, atomate2, jobflow, amset, jobflow_remote, and includes scripts (resource) and references (resource) components.

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.

Frequently Asked Questions about mat-dft-electronic-transport

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I calculate carrier mobility and the Seebeck coefficient from DFT using VASP?▼

Calculate carrier mobility and the Seebeck coefficient from DFT by coupling VASP band structures with AMSET in an atomate2 workflow, which automates structure relaxation, deformation potential evaluation, and transport output extraction.

Can I compute electrical conductivity for doped semiconductors across multiple temperatures with AMSET?▼

Compute electrical conductivity for doped semiconductors across multiple temperatures with AMSET by generating a VaspAmsetMaker flow, executing it through jobflow on HPC infrastructure, and parsing the produced transport JSON outputs.

What's the best way to automate an end-to-end electronic transport workflow in atomate2?▼

Automate an end-to-end electronic transport workflow in atomate2 by building a DAG that includes dense uniform band structure extraction, elastic tensor evaluation, and deformation potential calculations before final AMSET execution.

Do I need jobflow and HPC infrastructure to run AMSET transport calculations?▼

You need jobflow and HPC infrastructure to run AMSET transport calculations because the workflow orchestrates dense band structure extraction and deformation potential evaluations through jobflow_remote on appropriate high-performance computing resources.

How does first-principles electronic transport via DFT compare to empirical transport models?▼

First-principles electronic transport via DFT computes properties directly from calculated band structures and scattering physics using AMSET, bypassing the pre-fitted empirical parameters required by traditional empirical transport models.

How do I generate the transport workflow DAG for my structure?▼

Generate the transport workflow DAG for your structure by running the provided generate_inputs.py script, which outputs an amset_flow.json file ready for execution through your jobflow or Fireworks environment.