AniSOAP/CITATION.cff at main · cersonsky-lab/AniSOAP · GitHub

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

cff-version: 1.2.0
title: AniSOAP
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Arthur
    family-names: Lin
    email: alin62@wisc.edu
    affiliation: University of Wisconsin-Madison
    orcid: 'https://orcid.org/0000-0002-7665-3767'
  - given-names: Lucas
    family-names: Ortengren
    orcid: 'https://orcid.org/0009-0002-8899-7513'
    affiliation: University of Wisconsin-Madison
    email: ortengren@wisc.edu
  - given-names: Seonwoo
    family-names: Hwang
    email: hwangsean1119@gmail.com
    affiliation: University of Wisconsin-Madison
  - given-names: Yong-Cheol
    family-names: Cho
    affiliation: University of Illinois Urbana-Champaign
    orcid: 'https://orcid.org/0009-0001-6038-6764'
  - given-names: Jigyasa
    family-names: Nigam
    orcid: 'https://orcid.org/0000-0001-6857-4332'
  - given-names: Rose
    family-names: Cersonsky
    email: rose.cersonsky@wisc.edu
    affiliation: University of Wisconsin-Madison
    orcid: 'https://orcid.org/0000-0003-4515-3441'
identifiers:
  - type: url
    value: 'https://github.com/cersonsky-lab/AniSOAP'
repository-code: 'https://github.com/cersonsky-lab/AniSOAP'
url: 'https://anisoap.readthedocs.io/en/latest/'
abstract: >-
  `AniSOAP` is a package that creates Machine Learning (ML)
  representations of non-spherical particle configurations;
  these representations can then be used in ML-driven
  simulations and analyses. This generalization of existing
  spherical ML representations therefore aims to bridge the
  gap between two scientific communities: The
  machine-learned atomistic simulation community, whose
  primary concern is obtaining fast and (quantum) accurate
  descriptions of the complex interactions occuring between
  (spherical) atoms, and the coarse-grained and colloid
  modeling community, whose primary concern is understanding
  emergent behavior of macroscopic particles with
  (plausibly) complex geometries. `AniSOAP` provide a common
  framework to answer scientific questions at the
  intersection of these two fields.
keywords:
  - Machine Learning
  - Anisotropy
  - Machine Learning Potentials
  - Molecular Simulation
license: Apache-2.0