high-throughput

name	high-throughput
description	High-Throughput Computational Workflows (8 sub-skills: batch-calculations, batch-screening, convergence-automation, materials-filtering, matpes-dual-static, phase-stability, property-prediction, screening-workflow)

High-Throughput Computational Workflows

Sub-skills

Skill	Path	Description
Screening Workflow	screening-workflow/SKILL.md	End-to-end materials screening: query Materials Project for candidates, batch MACE relaxation, filter by stability/band gap/elastic modulus, rank candidates, validate top hits with DFT
Batch Calculations	batch-calculations/SKILL.md	Run batch DFT calculations on multiple structures: input generation with pymatgen sets, parallel execution, result collection, error handling and restart logic for QE and VASP
Phase Stability	phase-stability/SKILL.md	Phase stability and convex hull analysis: compute formation energies, build convex hulls, determine energy above hull, phase diagram sections, integration with Materials Project reference data
Property Prediction	property-prediction/SKILL.md	Rapid MACE-based property prediction pipeline: lattice constants, bulk modulus, DOS-based band gap proxy, surface energy, vacancy formation energy, comparison with databases, correlation plots
Batch Screening	batch-screening/SKILL.md	High-throughput screening workflow: fetch candidates from Materials Project, rapid MACE screening, filter/rank, detailed QE for top hits
Materials Filtering	materials-filtering/SKILL.md	Query and filter the Materials Project database: chemical system search, property ranges, stability criteria, export structures and data
Convergence Automation	convergence-automation/SKILL.md	Automated DFT convergence testing: sweep ecutwfc and k-grids, find optimal parameters via energy-per-atom threshold (1 meV/atom), combined cutoff-then-kgrid workflow, batch mode for multiple materials, convergence plots and JSON reports. Inspired by pyiron_atomistics ConvEncutParallel/ConvKpointParallel.
MatPES Dual Static	matpes-dual-static/SKILL.md	Dual-functional PBE + r2SCAN static calculations: run PBE first, reuse wavefunction for efficient r2SCAN, optional PBE+U, collect energies at two levels of theory for ML training data or cross-validation

General Pattern

1. Source candidates -- query Materials Project or build structures programmatically.
2. Fast screen -- use MACE-MP-0 to relax and evaluate properties in seconds per structure.
3. Filter and rank -- apply thermodynamic / electronic / mechanical criteria.
4. Validate -- run Quantum ESPRESSO (or VASP) DFT on the short-listed candidates.
5. Report -- export CSV/JSON, generate plots, build property tables.

Method Decision Guide

What kind of high-throughput task?

Screen many candidates for a target property?
  --> screening-workflow/ (full pipeline with MP query + MACE + DFT validation)

Run DFT on a known set of structures?
  --> batch-calculations/ (input generation, parallel execution, error handling)

Assess thermodynamic stability of candidate phases?
  --> phase-stability/ (convex hull, formation energies, decomposition analysis)

Rapidly predict multiple properties for a set of structures?
  --> property-prediction/ (MACE-based lattice constants, bulk modulus, surface energy, etc.)

Just query/filter the Materials Project database?
  --> materials-filtering/ (database queries, property ranges, export)

Need both PBE and r2SCAN energies efficiently (ML training data, cross-validation)?
  --> matpes-dual-static/ (PBE wavefunction warm-starts r2SCAN, optional PBE+U)

Determine optimal ecutwfc / k-grid for a new material or pseudopotential?
  --> convergence-automation/ (automated sweeps, threshold-based selection, batch mode)

Prerequisites

• MP_API_KEY environment variable set (obtain from https://next-gen.materialsproject.org/api).
• Python packages: mp-api, pymatgen, ase, mace-torch, numpy, pandas, matplotlib.
• For DFT validation: Quantum ESPRESSO 7.5 (pw.x). VASP support via external access in future.
• Optional: pip install fireworks jobflow atomate2 custodian for advanced workflow management.