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8 changes: 4 additions & 4 deletions paper/fig/fig_sparc_api_overview.svg
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76 changes: 75 additions & 1 deletion paper/paper.bib
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Expand Up @@ -259,7 +259,7 @@ @article{bonomi_plumed_2019
volume = {16},
rights = {2019 Springer Nature America, Inc.},
issn = {1548-7105},
url = {https://www.nature.com/articles/s41592-019-0506-8},
url = {http://dx.doi.org/10.1038/s41592-019-0506-8},
doi = {10.1038/s41592-019-0506-8},
pages = {670--673},
number = {8},
Expand Down Expand Up @@ -644,3 +644,77 @@ @article{smith_psi4_2020
url = {http://dx.doi.org/10.1063/5.0006002},
publisher = {AIP Publishing}
}

@article{musielewicz_finetuna_2022,
author = {Musielewicz, Joseph and Wang, Xiaoxiao and Tian,
Tian and Ulissi, Zachary},
title = {FINETUNA: fine-tuning accelerated molecular
simulations},
journal = {Machine Learning: Science and Technology},
year = 2022,
volume = 3,
number = 3,
month = sep,
pages = {03LT01},
issn = {2632-2153},
doi = {10.1088/2632-2153/ac8fe0},
url = {http://dx.doi.org/10.1088/2632-2153/ac8fe0},
publisher = {IOP Publishing}
}

@misc{ilyes_mace_2023,
author = {Batatia, Ilyes and Benner, Philipp and Chiang, Yuan
and Elena, Alin M. and Kovács, Dávid P. and
Riebesell, Janosh and Advincula, Xavier R. and Asta,
Mark and Avaylon, Matthew and Baldwin, William
J. and Berger, Fabian and Bernstein, Noam and
Bhowmik, Arghya and Blau, Samuel M. and Cărare, Vlad
and Darby, James P. and De, Sandip and Della Pia,
Flaviano and Deringer, Volker L. and Elijošius,
Rokas and El-Machachi, Zakariya and Falcioni, Fabio
and Fako, Edvin and Ferrari, Andrea C. and
Genreith-Schriever, Annalena and George, Janine and
Goodall, Rhys E. A. and Grey, Clare P. and Grigorev,
Petr and Han, Shuang and Handley, Will and Heenen,
Hendrik H. and Hermansson, Kersti and Holm,
Christian and Jaafar, Jad and Hofmann, Stephan and
Jakob, Konstantin S. and Jung, Hyunwook and Kapil,
Venkat and Kaplan, Aaron D. and Karimitari, Nima and
Kermode, James R. and Kroupa, Namu and Kullgren,
Jolla and Kuner, Matthew C. and Kuryla, Domantas and
Liepuoniute, Guoda and Margraf, Johannes T. and
Magdău, Ioan-Bogdan and Michaelides, Angelos and
Moore, J. Harry and Naik, Aakash A. and Niblett,
Samuel P. and Norwood, Sam Walton and O'Neill, Niamh
and Ortner, Christoph and Persson, Kristin A. and
Reuter, Karsten and Rosen, Andrew S. and Schaaf,
Lars L. and Schran, Christoph and Shi, Benjamin
X. and Sivonxay, Eric and Stenczel, Tamás K. and
Svahn, Viktor and Sutton, Christopher and Swinburne,
Thomas D. and Tilly, Jules and van der Oord, Cas and
Varga-Umbrich, Eszter and Vegge, Tejs and Vondrák,
Martin and Wang, Yangshuai and Witt, William C. and
Zills, Fabian and Csányi, Gábor},
title = {A foundation model for atomistic materials
chemistry},
year = 2024,
doi = {10.48550/ARXIV.2401.00096},
url = {https://arxiv.org/abs/2401.00096},
keywords = {Chemical Physics (physics.chem-ph), Materials
Science (cond-mat.mtrl-sci), FOS: Physical sciences,
FOS: Physical sciences},
publisher = {arXiv},
copyright = {Creative Commons Attribution Non Commercial No
Derivatives 4.0 International}
}

@misc{fairchem_2024,
author = {},
title = {fairchem by FAIR Chemistry},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/FAIR-Chem/fairchem}},
url={https://github.com/FAIR-Chem/fairchem},
commit = {f22dbdf5498912e519459831ac58668566e38e67}
}
73 changes: 41 additions & 32 deletions paper/paper.md
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Expand Up @@ -102,15 +102,16 @@ allowing for the flexible treatment of systems in
any dimensionality.

In the past few years, the SPARC-X project
([https://github.com/SPARC-X](https://github.com/SPARC-X)) has
led efforts to develop an open-source, real-space DFT code that
is both user-friendly and competitive with state-of-the-art plane-wave
codes. The philosophy of the SPARC-X project is to provide codes that are highly
efficient and portable (i.e. straightforward to install and use across various computational
environments). The codes also seek to be user-friendly and developer-friendly
to facilitate the implementation of new algorithms. In line with this, SPARC-X offers real-space DFT
algorithms through two implementations: 1) Matlab-based M-SPARC
[@xu_m-sparc-1.0_2020; @zhang_m-sparc-2.0_2023] for algorithm
([https://github.com/SPARC-X](https://github.com/SPARC-X)) has led
efforts to develop an open-source, real-space DFT code that is both
user-friendly and competitive with state-of-the-art plane-wave
codes. The philosophy of the SPARC-X project is to provide codes that
are highly efficient and portable (i.e. straightforward to install and
use across various computational environments). The codes also seek to
be user-friendly and developer-friendly to facilitate the
implementation of new algorithms. In line with this, SPARC-X offers
real-space DFT algorithms through two implementations: 1) Matlab-based
M-SPARC [@xu_m-sparc-1.0_2020; @zhang_m-sparc-2.0_2023] for algorithm
prototyping and small-system simulations, with no external
dependencies other than Matlab itself, and 2) C/C++ based SPARC
[@xu_sparc-1.0_2021; @zhang_sparc-2.0_2024] for large-scale production
Expand All @@ -122,33 +123,41 @@ interactions, and advanced exchange-correlation (xc) functionals
method [@suryanarayana_sparc_sq_2018], cyclic/helical symmetry
[@sharma_sparc_cyclix_2021], real-space density functional
perturbation theory (DFPT) [@sharma_sparc_dfpt_2023], orbital-free DFT
(ODFT) [@ghosh_sparc_ofdft_2016],
on-the-fly machine-learning force fields (OTF-MLFF)
[@kumar_ofdft_delta_ml_2023; @timmerman_sparc_mlff_2024; @kumar_sparc_mlff_2024]. The rapid
(ODFT) [@ghosh_sparc_ofdft_2016], on-the-fly machine-learning force
fields (OTF-MLFF) [@kumar_ofdft_delta_ml_2023;
@timmerman_sparc_mlff_2024; @kumar_sparc_mlff_2024]. The rapid
development of SPARC has led to the need for a fully functional and
user-friendly interface that facilitates the use of SPARC with
high-throughput workflows. To address this, we introduce the SPARC-X-API,
a Python interface designed to bridge the SPARC code with a
wide range of scientific workflows. The SPARC-X-API builds upon the
high-throughput workflows. To address this, we introduce the
SPARC-X-API, a Python interface designed to bridge the SPARC code with
a wide range of scientific workflows. The SPARC-X-API builds upon the
Python wrapper originally shipped with SPARC version 1.0
[@xu_sparc-1.0_2021], offering an API compatible with the widely-used
ASE (ASE [@larsen_ase_2017]) standard and
updated with the latest versions of SPARC. With ASE's support for
various popular DFT methods, including both plane-wave (e.g. VASP
[@kresse_vasp_1996], Quantum ESPRESSO [@giannozzi_qe_2017], and Abinit
[@gonze_abinit_2020]), and real-space (e.g. GPAW
[@enkovaara_gpaw_1_2011; @mortensen_gpaw_2_2024] and Octopus
[@tancogne_dejean_octopus_2020]) implementations, SPARC-X-API enables
seamless integration of SPARC into existing workflows, allowing users
to incorporate real-space DFT calculations with minimal adjustments. A summary of the role
ASE (ASE [@larsen_ase_2017]) standard and updated with the latest
versions of SPARC. With ASE's support for various popular DFT methods,
including both plane-wave (e.g. VASP [@kresse_vasp_1996], Quantum
ESPRESSO [@giannozzi_qe_2017], and Abinit [@gonze_abinit_2020]), and
real-space (e.g. GPAW [@enkovaara_gpaw_1_2011; @mortensen_gpaw_2_2024]
and Octopus [@tancogne_dejean_octopus_2020]) implementations,
SPARC-X-API enables seamless integration of SPARC into existing
workflows, allowing users to incorporate real-space DFT calculations
with minimal adjustments. The modular design of SPARC-X-API makes it
straightforward to be plugged into complex computational workflows,
for example high-throughput dynamics simulations by i-PI
[@litman_i-pi-3.0_2024] and PLUMED [@article{bonomi_plumed_2019], as
well as active machine learning frameworks including FineTuna
[@musielewicz_finetuna_2022], powered by state-of-art neural network
interatomic potentials such as FAIR-Chem
(https://github.com/FAIR-Chem/fairchem)[https://github.com/FAIR-Chem/fairchem]
and MACE-MP [@ilyes_mace_2023] model series. A summary of the role
SPARC-X-API in the SPARC-X project is shown in
\autoref{fig:sparc-overview}.
In addition to the capabilities inherited from ASE, SPARC-X-API seeks
to enhance the user experience in a few key aspects, including 1)
supporting SPARC-specific features in an ASE-comatible API, 2) a
parameter validation mechanism based on SPARC's `LaTeX` documentation,
and 3) a versatile socket communication layer for efficient
high-throughput calculations. Details will be discussed in the Features and Functionalities section.
\autoref{fig:sparc-overview}. In addition to the capabilities
inherited from ASE, SPARC-X-API seeks to enhance the user experience
in a few key aspects, including 1) supporting SPARC-specific features
in an ASE-comatible API, 2) a parameter validation mechanism based on
SPARC's `LaTeX` documentation, and 3) a versatile socket communication
layer for efficient high-throughput calculations. Details will be
discussed in the Features and Functionalities section.

<!-- Firstly, the -->
<!-- design of the API is closely aligned with the ASE interfaces of other -->
Expand Down Expand Up @@ -181,7 +190,7 @@ high-throughput calculations. Details will be discussed in the Features and Func

![Overview of SPARC-X-API in the SPARC-X project system
\label{fig:sparc-overview}
](fig/fig_sparc_api_overview.svg){ width=100% }
](fig/fig_sparc_api_overview.svg){ width=90% }



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