diff --git a/joss.07085/10.21105.joss.07085.crossref.xml b/joss.07085/10.21105.joss.07085.crossref.xml new file mode 100644 index 0000000000..5adbc987de --- /dev/null +++ b/joss.07085/10.21105.joss.07085.crossref.xml @@ -0,0 +1,254 @@ + + + + 20241021074950-86b7b35a79e7bf983fc86a579dad89c9ec888be3 + 20241021074950 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 10 + 2024 + + + 9 + + 102 + + + + ASIMTools: A lightweight framework for scalable and +reproducible atomic simulations + + + + Mgcini Keith + Phuthi + https://orcid.org/0000-0002-0982-8635 + + + Emil + Annevelink + https://orcid.org/0000-0001-5035-7807 + + + Venkatasubramanian + Viswanathan + https://orcid.org/0000-0003-1060-5495 + + + + 10 + 21 + 2024 + + + 7085 + + + 10.21105/joss.07085 + + + 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.13952433 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/7085 + + + + 10.21105/joss.07085 + https://joss.theoj.org/papers/10.21105/joss.07085 + + + https://joss.theoj.org/papers/10.21105/joss.07085.pdf + + + + + + Atomic Simulation Recipes: A Python framework +and library for automated workflows + Gjerding + Computational Materials +Science + 199 + 10.1016/j.commatsci.2021.110731 + 0927-0256 + 2021 + Gjerding, M., Skovhus, T., Rasmussen, +A., Bertoldo, F., Larsen, A. H., Mortensen, J. J., & Thygesen, K. S. +(2021). Atomic Simulation Recipes: A Python framework and library for +automated workflows. Computational Materials Science, 199, 110731. +https://doi.org/10.1016/j.commatsci.2021.110731 + + + LAMMPS - a flexible simulation tool for +particle-based materials modeling at the atomic, meso, and continuum +scales + Thompson + Comp. Phys. Comm. + 271 + 10.1016/j.cpc.2021.108171 + 2022 + Thompson, A. P., Aktulga, H. M., +Berger, R., Bolintineanu, D. S., Brown, W. M., Crozier, P. S., Veld, P. +J. in ’t, Kohlmeyer, A., Moore, S. G., Nguyen, T. D., Shan, R., Stevens, +M. J., Tranchida, J., Trott, C., & Plimpton, S. J. (2022). LAMMPS - +a flexible simulation tool for particle-based materials modeling at the +atomic, meso, and continuum scales. Comp. Phys. Comm., 271, 108171. +https://doi.org/10.1016/j.cpc.2021.108171 + + + Open computational materials +science + Walsh + Nature Materials + 1 + 23 + 10.1038/s41563-023-01699-7 + 1476-4660 + 2024 + Walsh, A. (2024). Open computational +materials science. Nature Materials, 23(1), 16–17. +https://doi.org/10.1038/s41563-023-01699-7 + + + FireWorks: A dynamic workflow system designed +for high-throughput applications + Jain + Concurrency and Computation: Practice and +Experience + 17 + 27 + 10.1002/cpe.3505 + 1532-0634 + 2015 + Jain, A., Ong, S. P., Chen, W., +Medasani, B., Qu, X., Kocher, M., Brafman, M., Petretto, G., Rignanese, +G.-M., Hautier, G., Gunter, D., & Persson, K. A. (2015). FireWorks: +A dynamic workflow system designed for high-throughput applications. +Concurrency and Computation: Practice and Experience, 27(17), 5037–5059. +https://doi.org/10.1002/cpe.3505 + + + Python Materials Genomics (pymatgen): A +robust, open-source python library for materials +analysis + Ong + Computational Materials +Science + 68 + 10.1016/j.commatsci.2012.10.028 + 0927-0256 + 2013 + Ong, S. P., Richards, W. D., Jain, +A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., +Persson, K. A., & Ceder, G. (2013). Python Materials Genomics +(pymatgen): A robust, open-source python library for materials analysis. +Computational Materials Science, 68, 314–319. +https://doi.org/10.1016/j.commatsci.2012.10.028 + + + The atomic simulation environment—a 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 environment—a Python library for working with atoms. Journal +of Physics: Condensed Matter, 29(27), 273002. +https://doi.org/10.1088/1361-648X/aa680e + + + AiiDA 1.0, a scalable computational +infrastructure for automated reproducible workflows and data +provenance + Huber + Scientific Data + 1 + 7 + 10.1038/s41597-020-00638-4 + 2052-4463 + 2020 + Huber, S. P., Zoupanos, S., Uhrin, +M., Talirz, L., Kahle, L., Häuselmann, R., Gresch, D., Müller, T., +Yakutovich, A. V., Andersen, C. W., Ramirez, F. F., Adorf, C. S., +Gargiulo, F., Kumbhar, S., Passaro, E., Johnston, C., Merkys, A., +Cepellotti, A., Mounet, N., … Pizzi, G. (2020). AiiDA 1.0, a scalable +computational infrastructure for automated reproducible workflows and +data provenance. Scientific Data, 7(1), 300. +https://doi.org/10.1038/s41597-020-00638-4 + + + Accurate Surface and Finite-Temperature Bulk +Properties of Lithium Metal at Large Scales Using Machine Learning +Interaction Potentials + Phuthi + ACS Omega + 9 + 9 + 10.1021/acsomega.3c10014 + 2024 + Phuthi, M. K., Yao, A. M., Batzner, +S., Musaelian, A., Guan, P., Kozinsky, B., Cubuk, E. D., & +Viswanathan, V. (2024). Accurate Surface and Finite-Temperature Bulk +Properties of Lithium Metal at Large Scales Using Machine Learning +Interaction Potentials. ACS Omega, 9(9), 10904–10912. +https://doi.org/10.1021/acsomega.3c10014 + + + Vibrational Entropy and Free Energy of Solid +Lithium using Covariance of Atomic Displacements Enabled by Machine +Learning + Phuthi + 2024 + Phuthi, M. K., Huang, Y., Widom, M., +& Viswanathan, V. (2024). Vibrational Entropy and Free Energy of +Solid Lithium using Covariance of Atomic Displacements Enabled by +Machine Learning. arXiv. +http://arxiv.org/abs/2406.15491 + + + + + + diff --git a/joss.07085/10.21105.joss.07085.pdf b/joss.07085/10.21105.joss.07085.pdf new file mode 100644 index 0000000000..2d3c9e275c Binary files /dev/null and b/joss.07085/10.21105.joss.07085.pdf differ diff --git a/joss.07085/paper.jats/10.21105.joss.07085.jats b/joss.07085/paper.jats/10.21105.joss.07085.jats new file mode 100644 index 0000000000..8944b5b962 --- /dev/null +++ b/joss.07085/paper.jats/10.21105.joss.07085.jats @@ -0,0 +1,481 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +7085 +10.21105/joss.07085 + +ASIMTools: A lightweight framework for scalable and +reproducible atomic simulations + + + +https://orcid.org/0000-0002-0982-8635 + +Phuthi +Mgcini Keith + + + + +https://orcid.org/0000-0001-5035-7807 + +Annevelink +Emil + + + + +https://orcid.org/0000-0003-1060-5495 + +Viswanathan +Venkatasubramanian + + + + + +University of Michigan, United States + + + + +Carnegie Mellon University, United States + + + + +17 +6 +2024 + +9 +102 +7085 + +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 +atomic simulation +density functional theory +workflow + + + + + + Summary +

Atomic SIMulation Tools (ASIMTools) is a + lightweight workflow and simulation manager for reproducible atomistic + simulations on Unix-based systems. Within the framework, simulations + can be transferred across computing environments, DFT codes, + interatomic potentials and atomic structures. By using in-built or + user-defined python modules (called asimmodules) and utilities, users + can run simulation protocols and automatically scale them on slurm + based clusters or locally on their console. The core idea is to + separate the dependence of the atomistic potential/calculator, the + computing environment and the simulation protocol thereby allowing the + same simulation to be run with different calculators, atomic + structures or on different computers with just a change of one + parameter in an input file after initial setup. This is increasingly + necessary as benchmarking Machine Learning Interatomic Potentials has + become a core part of computational materials science. Input and + output files follow a simple standard format, usually yaml, providing + a simple interface that also acts as a record of the parameters used + in a simulation without having to edit python scripts. The minimal set + of requirements means any materials science codes can be incorporated + into an ASIMTools workflow in a unified way.

+ + + Statement of need +

Atomic simulations are a key component of modern day materials + science in both academia and industry. However, simulation protocols + and workflows used by researchers are typically difficult to transfer + to systems using different inputs, codes and computing environments. + It often involves rewriting entire scripts in different languages to + change from one type of atomistic potential or atomic structure to + another. This leads to poor reproducibility and inefficient transfer + of code from one researcher to the next. In addition, there exists a + zoo of tools and packages for atomic simulation with more being + developed every day + (Walsh, + 2024). There is however no unifying framework that can + encompass all these tools without significant software development + effort. Significant effort should not be necessary because while the + source of the fundamental outputs of atomistic potentials such as + energy, forces etc. may differ, simulation protocols built on these + outputs should converge towards the most accurate and computationally + efficient. ASIMTools focuses on this last aspect by introducing + asimmodules which are simply Python functions that act as simulation + protocols which have no dependence on a specific atomistic potential + or computational environment or atomic structure. Through iteration + and community input, these simulation protocols will hopefully + converge towards best practice and ensure reproducibility of + simulation results.

+

ASIMTools is for users interested in + performing atomistic calculations on UNIX-like operating systems + and/or on slurm-based High Performance Computing clusters. By defining + simulation protocols as “asimmodules”, they can be easily added to the + library of provided asimmodules and iterated on. The flexibility of + ASIMTools allows integration of any kind of simulation tools such as + the heavily used Atomic Simulation Environment + (Larsen + et al., 2017) pymatgen + (Ong + et al., 2013), LAMMPS + (Thompson + et al., 2022) etc. with examples provided. With the asimmodules + defined, users only need to provide a set of inputs in the form of + yaml files that define the parameters used for each simulation and are + therefore a concrete record of used parameters.

+ + + State of the Field +

There exist a number of popular workflow tools for atomistic + simulations such as Aiida + (Huber + et al., 2020), Fireworks + (Jain + et al., 2015) and many more. These tools provide frameworks for + constructing complex workflows with different underlying principles. + Some managers enforce strict rules that ensure that data obeys FAIR + principles and emphasizes data provenance and reproducibility. These + methods however tend to be fairly large packages with steep learning + curves. ASIMTools provides a simple interface as a starting point that + can transform any code into ASIMTools compatible code by simply + wrapping it in a function that returns a Python dictionary. Any such + code can work in ASIMTools and with a few extra steps, the protocol + can be made to support an arbitrary calculator and input atomic + structure.

+

In some workflow managers, such as Atomic Simulation Recipes + (Gjerding + et al., 2021), once workflows are built, it can often be + difficult to quickly change and iterate over key parameters such as + the choice of atomistic calculator or structure as they are + intrinsically built into the code. This is particularly challenging in + an age where machine learning models are becoming more popular. + Workflows involving machine learning interatomic potentials tend to + require the ability to repeat the same calculations on different + examples, using different calculators on different hardware + iteratively. This is where the value of ASIMTools lies in contrast to + more established workflows. ASIMTools is not designed to replace the + more powerful workflow managers but rather to supplement them. This is + achieved by providing unified inputs that can be easily integrated + into, for example, Aiida as Python functions/asimmodules while also + being a stand-alone lightweight workflow manager for simpler + cases.

+ + + Usage To-Date +

ASIMTools has been used in the benchmarking Machine Learning + Interatomic Potentials + (Phuthi, + Yao, et al., 2024) and creating a workflow for calculation of + vibrational properties of solids calculations + (Phuthi, + Huang, et al., 2024).

+ + + Conclusion and Availability +

The ASIMTools package is a powerful tool for building and executing + atomic simulation protocols locally and at scale on slurm-based HPC + infrastructure. The code is hosted on a public Github repository + (https://github.com/BattModels/asimtools) with a number of examples. + Asimmodules for common calculations are also implemented with + examples. Interested users are encouraged to submit issues, contact + developers and make pull requests, particularly for adding new + simulation protocols to the library.

+ + + Author Contribution Statement +

Conceptualization by Keith Phuthi. Coding and development by Keith + Phuthi and Emil Annevelink. Paper writing by Keith Phuthi. Project + management by all.

+ + + Acknowledgements +

We acknowledge feedback from Kian Pu, Lance Kavalsky, Hancheng Zhao + and Ziqi Wang.

+ + + + + + + + + GjerdingMorten + SkovhusThorbjørn + RasmussenAsbjørn + BertoldoFabian + LarsenAsk Hjorth + MortensenJens Jørgen + ThygesenKristian Sommer + + Atomic Simulation Recipes: A Python framework and library for automated workflows + Computational Materials Science + 202111 + 20231023 + 199 + 0927-0256 + https://www.sciencedirect.com/science/article/pii/S0927025621004584 + 10.1016/j.commatsci.2021.110731 + 110731 + + + + + + + ThompsonA. P. + AktulgaH. M. + BergerR. + BolintineanuD. S. + BrownW. M. + CrozierP. S. + VeldP. J. in ’t + KohlmeyerA. + MooreS. G. + NguyenT. D. + ShanR. + StevensM. J. + TranchidaJ. + TrottC. + PlimptonS. J. + + LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales + Comp. Phys. Comm. + 2022 + 271 + 10.1016/j.cpc.2021.108171 + 108171 + + + + + + + WalshAron + + Open computational materials science + Nature Materials + 202401 + 20240111 + 23 + 1 + 1476-4660 + https://www.nature.com/articles/s41563-023-01699-7 + 10.1038/s41563-023-01699-7 + 16 + 17 + + + + + + JainAnubhav + OngShyue Ping + ChenWei + MedasaniBharat + QuXiaohui + KocherMichael + BrafmanMiriam + PetrettoGuido + RignaneseGian-Marco + HautierGeoffroy + GunterDaniel + PerssonKristin A. + + FireWorks: A dynamic workflow system designed for high-throughput applications + Concurrency and Computation: Practice and Experience + 2015 + 27 + 17 + 1532-0634 + http://dx.doi.org/10.1002/cpe.3505 + 10.1002/cpe.3505 + 5037 + 5059 + + + + + + OngShyue Ping + RichardsWilliam Davidson + JainAnubhav + HautierGeoffroy + KocherMichael + CholiaShreyas + GunterDan + ChevrierVincent L. + PerssonKristin A. + CederGerbrand + + Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis + Computational Materials Science + 201302 + 20240517 + 68 + 0927-0256 + https://www.sciencedirect.com/science/article/pii/S0927025612006295 + 10.1016/j.commatsci.2012.10.028 + 314 + 319 + + + + + + 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 environment—a Python library for working with atoms + Journal of Physics: Condensed Matter + 201706 + 20240517 + 29 + 27 + 0953-8984 + https://dx.doi.org/10.1088/1361-648X/aa680e + 10.1088/1361-648X/aa680e + 273002 + + + + + + + HuberSebastiaan P. + ZoupanosSpyros + UhrinMartin + TalirzLeopold + KahleLeonid + HäuselmannRico + GreschDominik + MüllerTiziano + YakutovichAliaksandr V. + AndersenCasper W. + RamirezFrancisco F. + AdorfCarl S. + GargiuloFernando + KumbharSnehal + PassaroElsa + JohnstonConrad + MerkysAndrius + CepellottiAndrea + MounetNicolas + MarzariNicola + KozinskyBoris + PizziGiovanni + + AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance + Scientific Data + 202009 + 20231023 + 7 + 1 + 2052-4463 + https://www.nature.com/articles/s41597-020-00638-4 + 10.1038/s41597-020-00638-4 + 300 + + + + + + + PhuthiMgcini Keith + YaoArchie Mingze + BatznerSimon + MusaelianAlbert + GuanPinwen + KozinskyBoris + CubukEkin Dogus + ViswanathanVenkatasubramanian + + Accurate Surface and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction Potentials + ACS Omega + 202403 + 20240315 + 9 + 9 + https://doi.org/10.1021/acsomega.3c10014 + 10.1021/acsomega.3c10014 + 10904 + 10912 + + + + + + PhuthiMgcini Keith + HuangYang + WidomMichael + ViswanathanVenkatasubramanian + + Vibrational Entropy and Free Energy of Solid Lithium using Covariance of Atomic Displacements Enabled by Machine Learning + arXiv + 202406 + 20240625 + http://arxiv.org/abs/2406.15491 + + + + +