autoplex 0.0.4

Automated machine-learned Potential Landscape explorer

autoplex is a software for generating and benchmarking machine learning (ML)-based interatomic potentials. The aim of autoplex is to provide a fully automated solution for creating high-quality ML potentials. The software is interfaced to multiple different ML potential fitting frameworks and to the atomate2 and ase environments for efficient high-throughput computations. The vision of this project is to allow a wide community of researchers to create accurate and reliable ML potentials for materials simulations.

autoplex is developed jointly by two research groups at BAM Berlin and the University of Oxford.

autoplex is an evolving project and contributions are very welcome! To ensure that the code remains of high quality, please raise a pull request for any contributions, which will be reviewed before integration into the main branch of the code. Initially, @JaGeo will handle the reviews.

Documentation

You can find the autoplex documentation here! The documentation also contains tutorials that teach you how to use autoplex for different use cases.

Before you start using autoplex

We expect the general user of autoplex to be familiar with the Materials Project framework software tools and related packages for (high-throughput) workflow submission and management. This involves the following software packages:

• pymatgen for input and output handling of computational materials science software
• atomate2 for providing a library of pre-defined computational materials science workflows
• jobflow for processes, job and workflow handling
• jobflow-remote or FireWorks for workflow and database (MongoDB) management
• MongoDB as the database (we recommend installing the MongoDB community version)

All of these software tools provide documentation and tutorials. Please take your time and check everything out!

Setup

To set up the mandatory prerequisites for using autoplex, please follow the installation guide of atomate2.

After setting up atomate2, make sure to add VASP_INCAR_UPDATES: {"NPAR": number} in your ~/atomate2/config/atomate2.yaml file. Set a number that is a divisor of the number of tasks you use for the VASP calculations.

Installation

Python version

Before the installation, please make sure that you are using one of the supported Python versions (see pyproject.toml)

Standard installation

Please install autoplex using pip install git+https://github.com/autoatml/autoplex.git. This will install all the Python packages and dependencies needed for MLIP fits. We will release a version of autoplex to PyPI in the next few weeks.

Additionally, to fit and validate ACEpotentials, one also needs to install Julia, as Autoplex relies on ACEpotentials, which supports fitting of linear ACE. Currently, no Python package exists for the same. Please run the following commands to enable the ACEpotentials fitting options and further functionality.

Install Julia v1.9.2

curl -fsSL https://install.julialang.org | sh -s -- default-channel 1.9.2

Once installed in the terminal, run the following commands to get Julia ACEpotentials dependencies.

julia -e 'using Pkg; Pkg.Registry.add("General"); Pkg.Registry.add(Pkg.Registry.RegistrySpec(url="https://github.com/ACEsuit/ACEregistry")); Pkg.add(Pkg.PackageSpec(;name="ACEpotentials", version="0.6.7")); Pkg.add("DataFrames"); Pkg.add("CSV")'

Enabling RSS workflows

Additionally, buildcell as a part of AIRSS needs to be installed if one wants to use the RSS functionality:

curl -O https://www.mtg.msm.cam.ac.uk/files/airss-0.9.3.tgz; tar -xf airss-0.9.3.tgz; rm airss-0.9.3.tgz; cd airss; make ; make install ; make neat; cd ..

Contributing guidelines / Developer's installation

A short guide to contributing to autoplex can be found here. Additional information for developers can be found here.

Workflow overview

We currently have two different types of automation workflows available:

• Workflow to use random-structure searches for the systematic construction of interatomic potentials. The implementation automates ideas from the following articles: Phys. Rev. Lett. 120, 156001 (2018) and npj Comput. Mater. 5, 99 (2019).
• Workflow to train accurate interatomic potentials for harmonic phonon properties. The implementation automates the ideas from the following article: J. Chem. Phys. 153, 044104 (2020).