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potential_publication

Repository for workflows associated with potentials publication

https://github.com/pyiron/potential_publication

Science Score: 67.0%

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    Found 4 DOI reference(s) in README
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    Links to: arxiv.org
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    Low similarity (10.9%) to scientific vocabulary
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Repository

Repository for workflows associated with potentials publication

Basic Info
  • Host: GitHub
  • Owner: pyiron
  • License: bsd-3-clause
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 74.4 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 1
  • Open Issues: 3
  • Releases: 1
Created about 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Code of conduct Citation

README.md

Workflows for "From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron"

Sarath Menon, Yury Lysogorskiy, Alexander L. M. Knoll, Niklas Leimeroth, Marvin Poul, Minaam Qamar, Jan Janssen, Matous Mrovec, Jochen Rohrer, Karsten Albe, Jörg Behler, Ralf Drautz, Jörg Neugebauer

Preprint at http://arxiv.org/abs/2403.05724 (2024)
Dataset at https://doi.org/10.17617/3.VKQ3ZM (2024)

This repository contains the workflows for the above publication.

Setting up the environment

The computational environment with all the necessary software can be installed using conda. First step is to create an environment:

conda env create -f binder/environment.yml

After the environment is created, it can be activated by

conda activate potentials

Once the environment is activated, the Tensorpot can be installed:

git clone --depth 1 --single-branch https://github.com/ICAMS/TensorPotential cd TensorPotential python setup.py install cd .. after this, jupyter lab can be started with

jupyter lab

Setting up pyiron

Create a configuration file called .pyiron in your home directory and add the following contents

[DEFAULT] FILE = ~/pyiron.db PROJECT_PATHS = ~/pyiron/projects RESOURCE_PATHS = ~/pyiron/resources:~/<path-to-repo>/potential_publication/resources

Note that <path-to-repo> needs to be replaced with the actual path.

Interatomic potentials used in this work

The files for the interatomic potentials used in this work is available in the resources/lammps/potentials folder.

Contents

  • 01datageneration: workflows for data generation for parametrising interatomic potentials, and reproducing figures from the publication.
  • 02_fitting: Workflows for fitting classical and ML potentials.
  • 03_validation: Workflows for validating potentials including EV curves, elastic constants, and phonon density of states.
  • 04phasediagram: Workflows for calcuting phase diagrams for EAM, HDNNP, and ACE potentials.

Owner

  • Name: pyiron
  • Login: pyiron
  • Kind: organization

pyiron - an integrated development environment (IDE) for materials science.

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Workflows for "From electrons to phase diagrams with
  classical and machine learning potentials: automated
  workflows for materials science with pyiron"
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Sarath
    family-names: Menon
    email: s.menon@mpie.de
    affiliation: Max-Planck-Institut für Eisenforschung GmbH
    orcid: 'https://orcid.org/0000-0002-6776-1213'
  - given-names: Yury
    family-names: Lysogorskiy
    affiliation: 'ICAMS, Ruhr-Universität Bochum'
  - given-names: Alexander
    name-particle: L. M.
    family-names: Knoll
    affiliation: >-
      Lehrstuhl für Theoretische Chemie II, Ruhr-Universität
      Bochum
  - given-names: Niklas
    family-names: Leimeroth
    orcid: 'https://orcid.org/0009-0005-3906-4751'
    affiliation: >-
      Technische Universität Darmstadt, Fachbereich Material
      und Geowissenschaften, Fachgebiet Materialmodellierung
  - given-names: Marvin
    family-names: Poul
    affiliation: Max-Planck-Institut für Eisenforschung GmbH
    orcid: 'https://orcid.org/0000-0002-6029-8748'
  - given-names: Minaam
    family-names: Qamar
    orcid: 'https://orcid.org/0000-0002-3342-4307'
    affiliation: 'ICAMS, Ruhr-Universität Bochum'
  - given-names: Jan
    family-names: Janssen
    orcid: 'https://orcid.org/0000-0001-9948-7119'
    affiliation: Max-Planck-Institut für Eisenforschung GmbH
  - given-names: Matous
    family-names: Mrovec
    orcid: 'https://orcid.org/0000-0001-8216-2254'
    affiliation: 'ICAMS, Ruhr-Universität Bochum'
  - given-names: Jochen
    family-names: Rohrer
    affiliation: >-
      Technische Universität Darmstadt, Fachbereich Material
      und Geowissenschaften, Fachgebiet Materialmodellierung
    orcid: 'https://orcid.org/0000-0002-4492-3371'
  - given-names: Karsten
    family-names: Albe
    orcid: 'https://orcid.org/0000-0003-4669-8056'
    affiliation: >-
      Technische Universität Darmstadt, Fachbereich Material
      und Geowissenschaften, Fachgebiet Materialmodellierung
  - given-names: Jörg
    family-names: Behler
    orcid: 'https://orcid.org/0000-0002-1220-1542'
    affiliation: >-
      Lehrstuhl für Theoretische Chemie II, Ruhr-Universität
      Bochum
  - given-names: Ralf
    family-names: Drautz
    orcid: 'https://orcid.org/0000-0001-7101-8804'
    affiliation: 'ICAMS, Ruhr-Universität Bochum'
  - given-names: Jörg
    family-names: Neugebauer
    affiliation: Max-Planck-Institut für Eisenforschung GmbH
    orcid: 'https://orcid.org/0000-0002-7903-2472'
identifiers:
  - type: doi
    value: 10.48550/arXiv.2403.05724
    description: ArXiv
  - type: doi
    value: 10.17617/3.VKQ3ZM
    description: Associated dataset
repository-code: 'https://github.com/pyiron/potential_publication'
repository-artifact: 'https://doi.org/10.17617/3.VKQ3ZM'
abstract: >-
  We present a comprehensive and user-friendly framework
  built upon the pyiron integrated development environment
  (IDE), enabling researchers to perform the entire Machine
  Learning Potential (MLP) development cycle consisting of
  (i) creating systematic DFT databases, (ii) fitting the
  Density Functional Theory (DFT) data to empirical
  potentials or MLPs, and (iii) validating the potentials in
  a largely automatic approach. The power and performance of
  this framework are demonstrated for three conceptually
  very different classes of interatomic potentials: an
  empirical potential (embedded atom method - EAM), neural
  networks (high-dimensional neural network potentials -
  HDNNP) and expansions in basis sets (atomic cluster
  expansion - ACE). As an advanced example for validation
  and application, we show the computation of a binary
  composition-temperature phase diagram for Al-Li, a
  technologically important lightweight alloy system with
  applications in the aerospace industry.
license: BSD-3-Clause-Attribution

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Dependencies

.github/ci_support/environment.yml conda
  • jupyter-book
binder/environment.yml pypi
  • pychromatic *
  • runnerase *
  • tensorflow *