high-throughput-dft

High-Throughput DFT Workflows

Description

This skill covers automated, large-scale DFT screening workflows: workflow graph construction and provenance tracking with atomate2/jobflow or AiiDA, input-set standardization, error handling with custodian, HPC job submission and restart logic, MongoDB-backed results storage, deduplication, and quality control. It connects high-throughput DFT to materials databases, ML training datasets, active learning loops, and surrogate screening campaigns. Invoke this skill when running hundreds to hundreds of thousands of DFT calculations systematically, automating a multi-step DFT pipeline (relax → static → DOS/bands → phonon → defect), or coupling DFT calculations to ML potential training or property screening.

Domain Context

High-throughput DFT (HT-DFT) is DFT run at scale: the same code, functional, and input protocol applied uniformly to a large structure library so that computed properties are mutually comparable. The value is not any single calculation but the internally consistent dataset that enables thermodynamic screening, property prediction, ML training, and active learning. This internal consistency is fragile — a single inconsistent INCAR, a different POTCAR version, or a missing convergence check can silently corrupt the comparative validity of hundreds of results.

Workflow orchestration: A DFT workflow is a directed acyclic graph (DAG) of calculations where each node is a DFT job and each edge is a data dependency. Orchestration tools (atomate2 + jobflow, FireWorks, AiiDA) manage job submission, output parsing, provenance recording, error detection, and chaining. The key distinction is between workflow definition (the graph and input sets) and workflow execution (the compute infrastructure that runs jobs). Separating these allows the same workflow graph to run on different HPC systems.

Input-set standardization: All DFT calculations in a screening campaign must use identical or explicitly documented input sets. The pymatgen InputSet machinery (MPRelaxSet, MPStaticSet, MPScanRelaxSet, MPHSEBSSet, etc.) encodes Materials Project–compatible calculation parameters, including POTCAR selection, INCAR defaults, k-mesh generation, and structure transformation. Using these sets ensures that computed energies are compatible with MP compatibility corrections and the MP thermodynamic database.