From 7dff0c7bcf2a3158c6d9e9cbbfca62c733cfefe4 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Mon, 21 Oct 2024 08:50:39 +0100 Subject: [PATCH] Creating 10.21105.joss.07085.jats --- .../paper.jats/10.21105.joss.07085.jats | 481 ++++++++++++++++++ 1 file changed, 481 insertions(+) create mode 100644 joss.07085/paper.jats/10.21105.joss.07085.jats 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.

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