Skip to content

Commit

Permalink
Merge pull request #5096 from openjournals/joss.06264
Browse files Browse the repository at this point in the history 
Merging automatically
  • Loading branch information
@editorialbot
editorialbot authored Mar 6, 2024
2 parents 159e3a4 + 959b7c9 commit 34fc2af
Show file tree
Hide file tree
Showing 3 changed files with 975 additions and 0 deletions.
373 changes: 373 additions & 0 deletions joss.06264/10.21105.joss.06264.crossref.xml
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,373 @@
<?xml version="1.0" encoding="UTF-8"?>
<doi_batch xmlns="http://www.crossref.org/schema/5.3.1"
xmlns:ai="http://www.crossref.org/AccessIndicators.xsd"
xmlns:rel="http://www.crossref.org/relations.xsd"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
version="5.3.1"
xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schemas/crossref5.3.1.xsd">
<head>
<doi_batch_id>20240306T173143-5195558cb2f517b68f83a032bf32550dcb69585c</doi_batch_id>
<timestamp>20240306173143</timestamp>
<depositor>
<depositor_name>JOSS Admin</depositor_name>
<email_address>[email protected]</email_address>
</depositor>
<registrant>The Open Journal</registrant>
</head>
<body>
<journal>
<journal_metadata>
<full_title>Journal of Open Source Software</full_title>
<abbrev_title>JOSS</abbrev_title>
<issn media_type="electronic">2475-9066</issn>
<doi_data>
<doi>10.21105/joss</doi>
<resource>https://joss.theoj.org</resource>
</doi_data>
</journal_metadata>
<journal_issue>
<publication_date media_type="online">
<month>03</month>
<year>2024</year>
</publication_date>
<journal_volume>
<volume>9</volume>
</journal_volume>
<issue>95</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>calorine: A Python package for constructing and
sampling neuroevolution potential models</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Eric</given_name>
<surname>Lindgren</surname>
<ORCID>https://orcid.org/0000-0002-8549-6839</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Magnus</given_name>
<surname>Rahm</surname>
<ORCID>https://orcid.org/0000-0002-6777-0371</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Erik</given_name>
<surname>Fransson</surname>
<ORCID>https://orcid.org/0000-0001-5262-3339</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Fredrik</given_name>
<surname>Eriksson</surname>
<ORCID>https://orcid.org/0000-0002-7945-5483</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Nicklas</given_name>
<surname>Österbacka</surname>
<ORCID>https://orcid.org/0000-0002-6043-4607</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Zheyong</given_name>
<surname>Fan</surname>
<ORCID>https://orcid.org/0000-0002-2253-8210</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Paul</given_name>
<surname>Erhart</surname>
<ORCID>https://orcid.org/0000-0002-2516-6061</ORCID>
</person_name>
</contributors>
<publication_date>
<month>03</month>
<day>06</day>
<year>2024</year>
</publication_date>
<pages>
<first_page>6264</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.06264</identifier>
</publisher_item>
<ai:program name="AccessIndicators">
<ai:license_ref applies_to="vor">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="am">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="tdm">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
</ai:program>
<rel:program>
<rel:related_item>
<rel:description>Software archive</rel:description>
<rel:inter_work_relation relationship-type="references" identifier-type="doi">10.5281/zenodo.10723374</rel:inter_work_relation>
</rel:related_item>
<rel:related_item>
<rel:description>GitHub review issue</rel:description>
<rel:inter_work_relation relationship-type="hasReview" identifier-type="uri">https://github.com/openjournals/joss-reviews/issues/6264</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.06264</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.06264</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.06264.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="MüsSukPas23">
<article_title>Interatomic potentials: Achievements and
challenges</article_title>
<author>Müser</author>
<journal_title>Advances in Physics: X</journal_title>
<issue>1</issue>
<volume>8</volume>
<doi>10.1080/23746149.2022.2093129</doi>
<cYear>2023</cYear>
<unstructured_citation>Müser, M. H., Sukhomlinov, S. V.,
&amp; Pastewka, L. (2023). Interatomic potentials: Achievements and
challenges. Advances in Physics: X, 8(1), 2093129.
https://doi.org/10.1080/23746149.2022.2093129</unstructured_citation>
</citation>
<citation key="UnkChmSau21">
<article_title>Machine Learning Force Fields</article_title>
<author>Unke</author>
<journal_title>Chemical Reviews</journal_title>
<issue>16</issue>
<volume>121</volume>
<doi>10.1021/acs.chemrev.0c01111</doi>
<issn>0009-2665</issn>
<cYear>2021</cYear>
<unstructured_citation>Unke, O. T., Chmiela, S., Sauceda, H.
E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., &amp;
Müller, K.-R. (2021). Machine Learning Force Fields. Chemical Reviews,
121(16), 10142–10186.
https://doi.org/10.1021/acs.chemrev.0c01111</unstructured_citation>
</citation>
<citation key="FanZenZha21">
<article_title>Neuroevolution machine learning potentials:
Combining high accuracy and low cost in atomistic simulations and
application to heat transport</article_title>
<author>Fan</author>
<journal_title>Physical Review B</journal_title>
<issue>10</issue>
<volume>104</volume>
<doi>10.1103/PhysRevB.104.104309</doi>
<cYear>2021</cYear>
<unstructured_citation>Fan, Z., Zeng, Z., Zhang, C., Wang,
Y., Song, K., Dong, H., Chen, Y., &amp; Ala-Nissila, T. (2021).
Neuroevolution machine learning potentials: Combining high accuracy and
low cost in atomistic simulations and application to heat transport.
Physical Review B, 104(10), 104309.
https://doi.org/10.1103/PhysRevB.104.104309</unstructured_citation>
</citation>
<citation key="Fan22">
<article_title>Improving the accuracy of the neuroevolution
machine learning potential for multi-component systems</article_title>
<author>Fan</author>
<journal_title>Journal of Physics: Condensed
Matter</journal_title>
<issue>12</issue>
<volume>34</volume>
<doi>10.1088/1361-648X/ac462b</doi>
<issn>0953-8984</issn>
<cYear>2022</cYear>
<unstructured_citation>Fan, Z. (2022). Improving the
accuracy of the neuroevolution machine learning potential for
multi-component systems. Journal of Physics: Condensed Matter, 34(12),
125902. https://doi.org/10.1088/1361-648X/ac462b</unstructured_citation>
</citation>
<citation key="FanWanYin22">
<article_title>GPUMD: A package for constructing accurate
machine-learned potentials and performing highly efficient atomistic
simulations</article_title>
<author>Fan</author>
<journal_title>The Journal of Chemical
Physics</journal_title>
<issue>11</issue>
<volume>157</volume>
<doi>10.1063/5.0106617</doi>
<issn>0021-9606</issn>
<cYear>2022</cYear>
<unstructured_citation>Fan, Z., Wang, Y., Ying, P., Song,
K., Wang, J., Wang, Y., Zeng, Z., Xu, K., Lindgren, E., Rahm, J. M.,
Gabourie, A. J., Liu, J., Dong, H., Wu, J., Chen, Y., Zhong, Z., Sun,
J., Erhart, P., Su, Y., &amp; Ala-Nissila, T. (2022). GPUMD: A package
for constructing accurate machine-learned potentials and performing
highly efficient atomistic simulations. The Journal of Chemical Physics,
157(11), 114801.
https://doi.org/10.1063/5.0106617</unstructured_citation>
</citation>
<citation key="LiuBygFan23">
<article_title>Large-scale machine-learning molecular
dynamics simulation of primary radiation damage in
tungsten</article_title>
<author>Liu</author>
<journal_title>Physical Review B</journal_title>
<issue>5</issue>
<volume>108</volume>
<doi>10.1103/PhysRevB.108.054312</doi>
<cYear>2023</cYear>
<unstructured_citation>Liu, J., Byggmästar, J., Fan, Z.,
Qian, P., &amp; Su, Y. (2023). Large-scale machine-learning molecular
dynamics simulation of primary radiation damage in tungsten. Physical
Review B, 108(5), 054312.
https://doi.org/10.1103/PhysRevB.108.054312</unstructured_citation>
</citation>
<citation key="FraWikErh23">
<article_title>Phase transitions in inorganic halide
perovskites from machine-learned potentials</article_title>
<author>Fransson</author>
<journal_title>The Journal of Physical Chemistry
C</journal_title>
<issue>28</issue>
<volume>127</volume>
<doi>10.1021/acs.jpcc.3c01542</doi>
<cYear>2023</cYear>
<unstructured_citation>Fransson, E., Wiktor, J., &amp;
Erhart, P. (2023). Phase transitions in inorganic halide perovskites
from machine-learned potentials. The Journal of Physical Chemistry C,
127(28), 13773–13781.
https://doi.org/10.1021/acs.jpcc.3c01542</unstructured_citation>
</citation>
<citation key="FraRosEri23">
<article_title>Limits of the phonon quasi-particle picture
at the cubic-to-tetragonal phase transition in halide
perovskites</article_title>
<author>Fransson</author>
<journal_title>Communications Physics</journal_title>
<issue>1</issue>
<volume>6</volume>
<doi>10.1038/s42005-023-01297-8</doi>
<issn>2399-3650</issn>
<cYear>2023</cYear>
<unstructured_citation>Fransson, E., Rosander, P., Eriksson,
F., Rahm, J. M., Tadano, T., &amp; Erhart, P. (2023). Limits of the
phonon quasi-particle picture at the cubic-to-tetragonal phase
transition in halide perovskites. Communications Physics, 6(1), 1–7.
https://doi.org/10.1038/s42005-023-01297-8</unstructured_citation>
</citation>
<citation key="ShaDaiChe23">
<article_title>Phonon thermal transport in two-dimensional
PbTe monolayers via extensive molecular dynamics simulations with a
neuroevolution potential</article_title>
<author>Sha</author>
<journal_title>Materials Today Physics</journal_title>
<volume>34</volume>
<doi>10.1016/j.mtphys.2023.101066</doi>
<issn>2542-5293</issn>
<cYear>2023</cYear>
<unstructured_citation>Sha, W., Dai, X., Chen, S., Yin, B.,
&amp; Guo, F. (2023). Phonon thermal transport in two-dimensional PbTe
monolayers via extensive molecular dynamics simulations with a
neuroevolution potential. Materials Today Physics, 34, 101066.
https://doi.org/10.1016/j.mtphys.2023.101066</unstructured_citation>
</citation>
<citation key="LarMorBlo17">
<article_title>The atomic simulation environmenta Python
library for working with atoms</article_title>
<author>Larsen</author>
<journal_title>Journal of Physics: Condensed
Matter</journal_title>
<issue>27</issue>
<volume>29</volume>
<doi>10.1088/1361-648X/aa680e</doi>
<issn>0953-8984</issn>
<cYear>2017</cYear>
<unstructured_citation>Larsen, A. H., Mortensen, J. J.,
Blomqvist, J., Castelli, I. E., Christensen, R., Dułak, M., Friis, J.,
Groves, M. N., Hammer, B., Hargus, C., Hermes, E. D., Jennings, P. C.,
Jensen, P. B., Kermode, J., Kitchin, J. R., Kolsbjerg, E. L., Kubal, J.,
Kaasbjerg, K., Lysgaard, S., … Jacobsen, K. W. (2017). The atomic
simulation environmenta Python library for working with atoms. Journal
of Physics: Condensed Matter, 29(27), 273002.
https://doi.org/10.1088/1361-648X/aa680e</unstructured_citation>
</citation>
<citation key="EriFraErh19">
<article_title>The Hiphive Package for the Extraction of
High-Order Force Constants by Machine Learning</article_title>
<author>Eriksson</author>
<journal_title>Advanced Theory and
Simulations</journal_title>
<issue>5</issue>
<volume>2</volume>
<doi>10.1002/adts.201800184</doi>
<issn>2513-0390</issn>
<cYear>2019</cYear>
<unstructured_citation>Eriksson, F., Fransson, E., &amp;
Erhart, P. (2019). The Hiphive Package for the Extraction of High-Order
Force Constants by Machine Learning. Advanced Theory and Simulations,
2(5), 1800184.
https://doi.org/10.1002/adts.201800184</unstructured_citation>
</citation>
<citation key="Tog23">
<article_title>First-principles Phonon Calculations with
Phonopy and Phono3py</article_title>
<author>Togo</author>
<journal_title>Journal of the Physical Society of
Japan</journal_title>
<issue>1</issue>
<volume>92</volume>
<doi>10.7566/JPSJ.92.012001</doi>
<issn>0031-9015</issn>
<cYear>2023</cYear>
<unstructured_citation>Togo, A. (2023). First-principles
Phonon Calculations with Phonopy and Phono3py. Journal of the Physical
Society of Japan, 92(1), 012001.
https://doi.org/10.7566/JPSJ.92.012001</unstructured_citation>
</citation>
<citation key="TogChaTad23">
<article_title>Implementation strategies in phonopy and
Phono3py</article_title>
<author>Togo</author>
<journal_title>Journal of Physics: Condensed
Matter</journal_title>
<issue>35</issue>
<volume>35</volume>
<doi>10.1088/1361-648X/acd831</doi>
<issn>0953-8984</issn>
<cYear>2023</cYear>
<unstructured_citation>Togo, A., Chaput, L., Tadano, T.,
&amp; Tanaka, I. (2023). Implementation strategies in phonopy and
Phono3py. Journal of Physics: Condensed Matter, 35(35), 353001.
https://doi.org/10.1088/1361-648X/acd831</unstructured_citation>
</citation>
<citation key="Wan23">
<article_title>PyNEP</article_title>
<author>Wang</author>
<cYear>2023</cYear>
<unstructured_citation>Wang, J. (2023). PyNEP.
https://pynep.readthedocs.io/en/latest/</unstructured_citation>
</citation>
<citation key="Gab23">
<article_title>Gpyumd</article_title>
<author>Gabourie</author>
<cYear>2023</cYear>
<unstructured_citation>Gabourie, A. J. (2023). Gpyumd.
https://gpyumd.readthedocs.io</unstructured_citation>
</citation>
<citation key="EriFraLin23">
<article_title>Tuning the through-plane lattice thermal
conductivity in van der waals structures through rotational
(dis)ordering</article_title>
<author>Eriksson</author>
<journal_title>ACS Nano</journal_title>
<issue>24</issue>
<volume>17</volume>
<doi>10.1021/acsnano.3c09717</doi>
<cYear>2023</cYear>
<unstructured_citation>Eriksson, F., Fransson, E.,
Linderälv, C., Fan, Z., &amp; Erhart, P. (2023). Tuning the
through-plane lattice thermal conductivity in van der waals structures
through rotational (dis)ordering. ACS Nano, 17(24), 25565–25574.
https://doi.org/10.1021/acsnano.3c09717</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Loading

0 comments on commit 34fc2af

Please sign in to comment.