diff --git a/joss.06264/10.21105.joss.06264.crossref.xml b/joss.06264/10.21105.joss.06264.crossref.xml new file mode 100644 index 0000000000..08efcc7b18 --- /dev/null +++ b/joss.06264/10.21105.joss.06264.crossref.xml @@ -0,0 +1,373 @@ + + + + 20240306T173143-5195558cb2f517b68f83a032bf32550dcb69585c + 20240306173143 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 03 + 2024 + + + 9 + + 95 + + + + calorine: A Python package for constructing and +sampling neuroevolution potential models + + + + Eric + Lindgren + https://orcid.org/0000-0002-8549-6839 + + + Magnus + Rahm + https://orcid.org/0000-0002-6777-0371 + + + Erik + Fransson + https://orcid.org/0000-0001-5262-3339 + + + Fredrik + Eriksson + https://orcid.org/0000-0002-7945-5483 + + + Nicklas + Österbacka + https://orcid.org/0000-0002-6043-4607 + + + Zheyong + Fan + https://orcid.org/0000-0002-2253-8210 + + + Paul + Erhart + https://orcid.org/0000-0002-2516-6061 + + + + 03 + 06 + 2024 + + + 6264 + + + 10.21105/joss.06264 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10723374 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/6264 + + + + 10.21105/joss.06264 + https://joss.theoj.org/papers/10.21105/joss.06264 + + + https://joss.theoj.org/papers/10.21105/joss.06264.pdf + + + + + + Interatomic potentials: Achievements and +challenges + Müser + Advances in Physics: X + 1 + 8 + 10.1080/23746149.2022.2093129 + 2023 + Müser, M. H., Sukhomlinov, S. V., +& Pastewka, L. (2023). Interatomic potentials: Achievements and +challenges. Advances in Physics: X, 8(1), 2093129. +https://doi.org/10.1080/23746149.2022.2093129 + + + Machine Learning Force Fields + Unke + Chemical Reviews + 16 + 121 + 10.1021/acs.chemrev.0c01111 + 0009-2665 + 2021 + Unke, O. T., Chmiela, S., Sauceda, H. +E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & +Müller, K.-R. (2021). Machine Learning Force Fields. Chemical Reviews, +121(16), 10142–10186. +https://doi.org/10.1021/acs.chemrev.0c01111 + + + Neuroevolution machine learning potentials: +Combining high accuracy and low cost in atomistic simulations and +application to heat transport + Fan + Physical Review B + 10 + 104 + 10.1103/PhysRevB.104.104309 + 2021 + Fan, Z., Zeng, Z., Zhang, C., Wang, +Y., Song, K., Dong, H., Chen, Y., & 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 + + + Improving the accuracy of the neuroevolution +machine learning potential for multi-component systems + Fan + Journal of Physics: Condensed +Matter + 12 + 34 + 10.1088/1361-648X/ac462b + 0953-8984 + 2022 + 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 + + + GPUMD: A package for constructing accurate +machine-learned potentials and performing highly efficient atomistic +simulations + Fan + The Journal of Chemical +Physics + 11 + 157 + 10.1063/5.0106617 + 0021-9606 + 2022 + 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., & 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 + + + Large-scale machine-learning molecular +dynamics simulation of primary radiation damage in +tungsten + Liu + Physical Review B + 5 + 108 + 10.1103/PhysRevB.108.054312 + 2023 + Liu, J., Byggmästar, J., Fan, Z., +Qian, P., & 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 + + + Phase transitions in inorganic halide +perovskites from machine-learned potentials + Fransson + The Journal of Physical Chemistry +C + 28 + 127 + 10.1021/acs.jpcc.3c01542 + 2023 + Fransson, E., Wiktor, J., & +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 + + + Limits of the phonon quasi-particle picture +at the cubic-to-tetragonal phase transition in halide +perovskites + Fransson + Communications Physics + 1 + 6 + 10.1038/s42005-023-01297-8 + 2399-3650 + 2023 + Fransson, E., Rosander, P., Eriksson, +F., Rahm, J. M., Tadano, T., & 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 + + + Phonon thermal transport in two-dimensional +PbTe monolayers via extensive molecular dynamics simulations with a +neuroevolution potential + Sha + Materials Today Physics + 34 + 10.1016/j.mtphys.2023.101066 + 2542-5293 + 2023 + Sha, W., Dai, X., Chen, S., Yin, B., +& 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 + + + The atomic simulation environmenta Python +library for working with atoms + Larsen + Journal of Physics: Condensed +Matter + 27 + 29 + 10.1088/1361-648X/aa680e + 0953-8984 + 2017 + 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 + + + The Hiphive Package for the Extraction of +High-Order Force Constants by Machine Learning + Eriksson + Advanced Theory and +Simulations + 5 + 2 + 10.1002/adts.201800184 + 2513-0390 + 2019 + Eriksson, F., Fransson, E., & +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 + + + First-principles Phonon Calculations with +Phonopy and Phono3py + Togo + Journal of the Physical Society of +Japan + 1 + 92 + 10.7566/JPSJ.92.012001 + 0031-9015 + 2023 + 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 + + + Implementation strategies in phonopy and +Phono3py + Togo + Journal of Physics: Condensed +Matter + 35 + 35 + 10.1088/1361-648X/acd831 + 0953-8984 + 2023 + Togo, A., Chaput, L., Tadano, T., +& 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 + + + PyNEP + Wang + 2023 + Wang, J. (2023). PyNEP. +https://pynep.readthedocs.io/en/latest/ + + + Gpyumd + Gabourie + 2023 + Gabourie, A. J. (2023). Gpyumd. +https://gpyumd.readthedocs.io + + + Tuning the through-plane lattice thermal +conductivity in van der waals structures through rotational +(dis)ordering + Eriksson + ACS Nano + 24 + 17 + 10.1021/acsnano.3c09717 + 2023 + Eriksson, F., Fransson, E., +Linderälv, C., Fan, Z., & 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 + + + + + + diff --git a/joss.06264/10.21105.joss.06264.jats b/joss.06264/10.21105.joss.06264.jats new file mode 100644 index 0000000000..c95da37a07 --- /dev/null +++ b/joss.06264/10.21105.joss.06264.jats @@ -0,0 +1,602 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6264 +10.21105/joss.06264 + +calorine: A Python package for constructing and sampling +neuroevolution potential models + + + +https://orcid.org/0000-0002-8549-6839 + +Lindgren +Eric + + + + +https://orcid.org/0000-0002-6777-0371 + +Rahm +Magnus + + + + +https://orcid.org/0000-0001-5262-3339 + +Fransson +Erik + + + + +https://orcid.org/0000-0002-7945-5483 + +Eriksson +Fredrik + + + + +https://orcid.org/0000-0002-6043-4607 + +Österbacka +Nicklas + + + + +https://orcid.org/0000-0002-2253-8210 + +Fan +Zheyong + + + + +https://orcid.org/0000-0002-2516-6061 + +Erhart +Paul + + +* + + + +Department of Physics, Chalmers University of Technology, +Gothenburg 412 96, Sweden + + + + +College of Physical Science and Technology, Bohai +University, Jinzhou 121013, P. R. China + + + + +* E-mail: + + +20 +10 +2023 + +9 +95 +6264 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +condensed matter +machine learning +interatomic potentials +force fields +molecular dynamics +neuroevolution +neural network + + + + + + Summary +

Molecular dynamics (MD) simulations are a key tool in computational + chemistry, physics, and materials science, aiding the understanding of + microscopic processes but also guiding the development of novel + materials. A MD simulation requires a model for the interatomic + interactions. To this end, one traditionally often uses empirical + interatomic potentials or force fields, which are fast but inaccurate, + or ab-initio methods based on electronic structure theory such as + density functional theory, which are accurate but computationally very + expensive + (Müser + et al., 2023). Machine-learned interatomic potentials (MLIPs) + have in recent years emerged as an alternative to these approaches, + combining the speed of heuristic force fields with the accuracy of + ab-initio techniques + (Unke + et al., 2021). Neuroevolution potentials (NEPs), implemented in + the GPUMD package, in particular, are a highly + accurate and efficient class of MLIPs + (Fan + et al., 2021, + 2022; + Fan, + 2022). NEP models have already been used to study a variety of + properties in a range of materials, with recent examples including + radiation damage in tungsten + (Liu + et al., 2023), phase transitions + (Fransson, + Wiktor, et al., 2023) and dynamics of halide perovskites + (Fransson, + Rosander, et al., 2023) as well as thermal transport in + two-dimensional materials + (Sha + et al., 2023). Here, we present + calorine, a Python package that simplifies the + construction, analysis and use of NEP models via + GPUMD.

+ + + Statement of need +

GPUMD is a package written in C++/CUDA that + enables MD simulations as well as the construction of NEP models, with + all computations running on a discrete GPU. For efficiency reasons + this package uses a set of text based input and output files. + calorine provides a Python interface that makes + it easy to access the functionality of GPUMD + and integrate it in Python based workflows. This includes but is not + limited to managing the construction of NEP models as well as setting + up and analyzing MD simulations.

+

calorine also exposes two + ASE Calculator objects + (Larsen + et al., 2017), one using the CPU and one using the GPU. This + has the expressed purpose of making NEP models transferable for use + outside of GPUMD, since the calculators can be + used by other codes, as well as on machines without discrete GPUs. + Examples of such use cases include calculating force constants using + hiphive + (Eriksson + et al., 2019) and phonon dispersions using + phonopy + (Togo, + 2023; + Togo + et al., 2023).

+

The full documentation for calorine in + addition to examples and tutorials can be found at + https://calorine.materialsmodeling.org/.

+ + + Related software and recent work +

Two other software packages that serve as companion software for + GPUMD are PyNEP + (Wang, + 2023) and GPYUMD + (Gabourie, + 2023), focusing on NEP construction and + MD simulations within GPUMD respectively. + calorine differs from these two by having a + broader scope, encompassing both NEP + construction and sampling with MD simulations. Additionally, + calorine exposes an interface for modifying + potential files, further improving the transferability of + NEP.

+

Examples of recently published work supported by + calorine include a study of the through-plane + lattice thermal conductivity in van-der-Waals structures + (Eriksson + et al., 2023), and a study of dynamic modes in halide + perovskites under a continous-order phase transition + (Fransson, + Rosander, et al., 2023).

+ + + Acknowledgements +

We acknowledge and greatly appreciate contributions made by Petter + Rosander. This work was funded by the Swedish Research Council (Grant + Nos. 2018-06482, 2020-04935, and 2021-05072) as well as the Swedish + Foundation for Strategic Research (SSF) via the SwedNess program + (Grant No. GSn15-0008), and enabled by computational resources + provided by the National Academic Infrastructure for Supercomputing in + Sweden (NAISS) and the Swedish National Infrastructure for Computing + (SNIC) at C3SE, UPPMAX, and HPC2N partially funded by the Swedish + Research Council (Grant Nos. 2018-05973 and 2022-06725).

+ + + + + + + + MüserMartin H. + SukhomlinovSergey V. + PastewkaLars + + Interatomic potentials: Achievements and challenges + Advances in Physics: X + Taylor & Francis + 202312 + 20231020 + 8 + 1 + 10.1080/23746149.2022.2093129 + 2093129 + + + + + + + UnkeOliver T. + ChmielaStefan + SaucedaHuziel E. + GasteggerMichael + PoltavskyIgor + SchüttKristof T. + TkatchenkoAlexandre + MüllerKlaus-Robert + + Machine Learning Force Fields + Chemical Reviews + 202108 + 20211018 + 121 + 16 + 0009-2665 + 10.1021/acs.chemrev.0c01111 + 10142 + 10186 + + + + + + FanZheyong + ZengZezhu + ZhangCunzhi + WangYanzhou + SongKeke + DongHaikuan + ChenYue + Ala-NissilaTapio + + Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport + Physical Review B + American Physical Society + 202109 + 20211217 + 104 + 10 + 10.1103/PhysRevB.104.104309 + 104309 + + + + + + + FanZheyong + + Improving the accuracy of the neuroevolution machine learning potential for multi-component systems + Journal of Physics: Condensed Matter + IOP Publishing + 202201 + 20220208 + 34 + 12 + 0953-8984 + 10.1088/1361-648X/ac462b + 125902 + + + + + + + FanZheyong + WangYanzhou + YingPenghua + SongKeke + WangJunjie + WangYong + ZengZezhu + XuKe + LindgrenEric + RahmJ. Magnus + GabourieAlexander J. + LiuJiahui + DongHaikuan + WuJianyang + ChenYue + ZhongZheng + SunJian + ErhartPaul + SuYanjing + Ala-NissilaTapio + + GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations + The Journal of Chemical Physics + American Institute of Physics + 202209 + 20230221 + 157 + 11 + 0021-9606 + 10.1063/5.0106617 + 114801 + + + + + + + LiuJiahui + ByggmästarJesper + FanZheyong + QianPing + SuYanjing + + Large-scale machine-learning molecular dynamics simulation of primary radiation damage in tungsten + Physical Review B + American Physical Society + 202308 + 20230920 + 108 + 5 + 10.1103/PhysRevB.108.054312 + 054312 + + + + + + + FranssonErik + WiktorJulia + ErhartPaul + + Phase transitions in inorganic halide perovskites from machine-learned potentials + The Journal of Physical Chemistry C + 2023 + 127 + 28 + + https://doi.org/10.1021/acs.jpcc.3c01542 + + 10.1021/acs.jpcc.3c01542 + 13773 + 13781 + + + + + + FranssonErik + RosanderPetter + ErikssonFredrik + RahmJ. Magnus + TadanoTerumasa + ErhartPaul + + Limits of the phonon quasi-particle picture at the cubic-to-tetragonal phase transition in halide perovskites + Communications Physics + Nature Publishing Group + 202307 + 20231020 + 6 + 1 + 2399-3650 + 10.1038/s42005-023-01297-8 + 1 + 7 + + + + + + ShaWenhao + DaiXuan + ChenSiyu + YinBinglun + GuoFenglin + + Phonon thermal transport in two-dimensional PbTe monolayers via extensive molecular dynamics simulations with a neuroevolution potential + Materials Today Physics + 202305 + 20230920 + 34 + 2542-5293 + 10.1016/j.mtphys.2023.101066 + 101066 + + + + + + + LarsenAsk Hjorth + MortensenJens Jørgen + BlomqvistJakob + CastelliIvano E. + ChristensenRune + DułakMarcin + FriisJesper + GrovesMichael N. + HammerBjørk + HargusCory + HermesEric D. + JenningsPaul C. + JensenPeter Bjerre + KermodeJames + KitchinJohn R. + KolsbjergEsben Leonhard + KubalJoseph + KaasbjergKristen + LysgaardSteen + MaronssonJón Bergmann + MaxsonTristan + OlsenThomas + PastewkaLars + PetersonAndrew + RostgaardCarsten + SchiøtzJakob + SchüttOle + StrangeMikkel + ThygesenKristian S. + VeggeTejs + VilhelmsenLasse + WalterMichael + ZengZhenhua + JacobsenKarsten W. + + The atomic simulation environmenta Python library for working with atoms + Journal of Physics: Condensed Matter + IOP Publishing + 201706 + 20230404 + 29 + 27 + 0953-8984 + 10.1088/1361-648X/aa680e + 273002 + + + + + + + ErikssonFredrik + FranssonErik + ErhartPaul + + The Hiphive Package for the Extraction of High-Order Force Constants by Machine Learning + Advanced Theory and Simulations + 2019 + 20230920 + 2 + 5 + 2513-0390 + 10.1002/adts.201800184 + 1800184 + + + + + + + TogoAtsushi + + First-principles Phonon Calculations with Phonopy and Phono3py + Journal of the Physical Society of Japan + The Physical Society of Japan + 202301 + 20230920 + 92 + 1 + 0031-9015 + 10.7566/JPSJ.92.012001 + 012001 + + + + + + + TogoAtsushi + ChaputLaurent + TadanoTerumasa + TanakaIsao + + Implementation strategies in phonopy and Phono3py + Journal of Physics: Condensed Matter + IOP Publishing + 202306 + 20230920 + 35 + 35 + 0953-8984 + 10.1088/1361-648X/acd831 + 353001 + + + + + + + WangJunji + + PyNEP + 202310 + 20231020 + https://pynep.readthedocs.io/en/latest/ + + + + + + GabourieAlexander J. + + Gpyumd + 202310 + 20231020 + https://gpyumd.readthedocs.io + + + + + + ErikssonFredrik + FranssonErik + LinderälvChristopher + FanZheyong + ErhartPaul + + Tuning the through-plane lattice thermal conductivity in van der waals structures through rotational (dis)ordering + ACS Nano + 2023 + 17 + 24 + + https://doi.org/10.1021/acsnano.3c09717 + + 10.1021/acsnano.3c09717 + 25565 + 25574 + + + + +
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