Atomic Simulation Recipes -- a Python framework and library for automated workflows

TL;DR

The paper introduces Atomic Simulation Recipes (ASR), a Python framework that simplifies and automates atomistic materials simulations, enabling high-throughput workflows with data provenance and flexible integration with simulation codes.

Contribution

ASR provides a novel, high-level Python framework with reusable Recipes for atomistic simulations, enhancing automation, data management, and interoperability in materials research.

Findings

Supports high-throughput simulations with data provenance

Provides a library of Recipes for DFT and many-body methods

Enables flexible workflow automation and data management

Abstract

The Atomic Simulation Recipes (ASR) is an open source Python framework for working with atomistic materials simulations in an efficient and sustainable way that is ideally suited for high-throughput projects. Central to ASR is the concept of a Recipe: a high-level Python script that performs a well defined simulation task robustly and accurately while keeping track of the data provenance. The ASR leverages the functionality of the Atomic Simulation Environment (ASE) to interface with external simulation codes and attain a high abstraction level. We provide a library of Recipes for common simulation tasks employing density functional theory and many-body perturbation schemes. These Recipes utilize the GPAW electronic structure code, but may be adapted to other simulation codes with an ASE interface. Being independent objects with automatic data provenance control, Recipes can be freely combined through Python scripting giving maximal freedom for users to build advanced workflows. ASR also implements a command line interface that can be used to run Recipes and inspect results. The ASR Migration module helps users maintain their data while the Database and App modules makes it possible to create local databases and present them as customized web pages.