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wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows.

Elena Gelžinytė1, Simon Wengert2, Tamás K Stenczel1

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Summary
This summary is machine-generated.

New workflow tools, wfl and ExPyRe, streamline complex atomistic simulations and machine learning interatomic potential (MLIP) fitting. These packages enhance computational efficiency for diverse simulation tasks, aiding scientific discovery.

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Area of Science:

  • Computational materials science
  • Scientific computing
  • Machine learning in chemistry and physics

Background:

  • Atomistic simulations are crucial for high-throughput studies, relying on workflow management packages for efficiency.
  • Existing packages primarily support computationally intensive ab initio calculations, focusing on reproducibility.
  • Machine learning interatomic potential (MLIP) development has distinct computational needs, requiring flexible parallelization and execution strategies.

Purpose of the Study:

  • To introduce wfl, a workflow management package for atomistic simulations and MLIP fitting.
  • To present ExPyRe, a Python package for remote execution, complementing wfl.
  • To address the diverse computational requirements of MLIP development beyond traditional ab initio methods.

Main Methods:

  • Development of the wfl workflow management package.
  • Integration of the ExPyRe package for versatile remote execution capabilities.
  • Utilizing a low-level developer framework to build high-level, user-friendly functionalities for MLIP fitting automation.

Main Results:

  • wfl and ExPyRe enable the creation of versatile workflows for diverse atomistic simulation tasks.
  • The tools provide a framework for automating complex machine learning interatomic potential fitting procedures.
  • Demonstrated capabilities of wfl in showcasing automated MLIP fitting.

Conclusions:

  • wfl and ExPyRe meet the specific computational demands of MLIP development and diverse atomistic simulations.
  • These tools fill a critical niche, supporting efficient custom computational task development.
  • The packages are expected to accelerate advancements in simulation-driven scientific discovery.