[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124390-en":3,"doc-seo-124390-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124390,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",6,"Technology","wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows","Predictive atomistic simulations increasingly support data-intensive high-throughput studies that leverage expanding computational resources. Existing workflow managers often target computationally heavy ab initio tasks with strong emphasis on provenance and reproducibility, yet machine-learning interatomic potential (MLIP) workflows require different parallelization and local or remote execution strategies. This work introduces wfl, an atomistic simulation and MLIP fitting workflow management package, and ExPyRe, a Python remote execution package, enabling versatile, low-level framework-based automation.","RESEARCH ARTICLE | SEPTEMBER 28 2023  \nwfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows  \nSpecial Collection: Software for Atomistic Machine Learning  \nElena Gelžinytė 􀀧  ; Simon Wengert  ; Tamás K. Stenczel  ; Hendrik H. Heenen  ; Karsten Reuter  ; Gábor Csányi  ; Noam Bernstein   \nJ. Chem. Phys. 159, 124801 (2023)  \n[https://doi.org/10.1063/5.0156845](https://doi.org/10.1063/5.0156845)  \n􀀭  \nView Online  \n􀀱  \nExport Citation  \nCrossMark  \n31 January 2024 10:19:04  \nThe Journal  \nof Chemical Physics  \nARTICLE  \n[pubs.aip.org/aip/jcp](pubs.aip.org/aip/jcp)  \nwfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows  \n\n| Cite as: J. Chem. Phys. 159, 124801 (2023); doi: 10. 1063/5.0156845\u003Cbr>Submitted: 3 May 2023 • Accepted: 10 July 2023 •\u003Cbr>Published Online: 28 September 2023 • Publisher Corrected: 04 October 2023 |  | \u003Cbr> |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- |\n| Elena Gelinyt,1, a)  Simon Wengert,2  Tamás K. Stenczel,1  Gábor Csányi,1  and Noam Bernstein3  | Hendrik H. Heenen,2  |  | Karsten |  | Reuter,2  |  |\n| AFFILIATIONS\u003Cbr>1 Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom\u003Cbr>2 Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany\u003Cbr>3 Center for Materials Physics and Technology, U. S. Naval Research Laboratory Code 6393, 4555 Overlook Ave. SW, Maryland, Washington, DC 20375, USA\u003Cbr>Note: This paper is part of the JCP Special Topic on Software for Atomistic Machine Learning.\u003Cbr>a)Author to whom correspondence should [be addressed: eg475@cam.ac.uk](be addressed: eg475@cam.ac.uk) |  |  |  |  |  |  |\n| ABSTRACT\u003Cbr>Predictive atomistic simulations are increasingly employed for data intensive high throughput studies that take advantage of constantly growing computational resources. To handle the sheer number of individual calculations that are needed in such studies, workflow management packages for atomistic simulations have been developed for a rapidly growing user base. These packages are predominantly designed to handle computationally heavy ab initio calculations, usually with a focus on data provenance and reproducibility. However, in related simulation communities, e.g., the developers of machine learning interatomic potentials (MLIPs), the computational requirements are somewhat different: the types, sizes, and numbers of computational tasks are more diverse and, therefore, require additional ways of parallelization and local or remote execution for optimal efficiency. In this work, we present the atomistic simulation and MLIP fitting workflow management package wfl and Python remote execution package ExPyRe to meet these requirements. With wfl and ExPyRe, versatile atomic simulation environment based workflows that perform diverse pro\u003Cbr>cedures can be written. This capability is based on a low-level developer-oriented framework, which can be utilized to construct high level functionality for user-friendly programs. Such high level capabilities to automate machine learning interatomic potential fitting procedures are already incorporated in wfl, which we use to showcase its capabilities in this work. We believe that wfl fills an important niche in several growing simulation communities and will aid the development of efficient custom computational tasks.\u003Cbr>© 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)). [https://doi.org/10.1063/5.0156845](https://doi.org/10.1063/5.0156845)\u003Cbr> |  |  |  |  |  |  |\n\nI. INTRODUCTION  \nIt is common to perform a large number of expensive calculations in computational chemistry and materials science. In many cases, these tasks are embarrassingly parallel, i.e., each calculation can ","cbCaiaTUd3Doj6Ut","https://ap.wps.com/l/cbCaiaTUd3Doj6Ut","pdf",4971572,1,12,"English","en",105,"# Abstract\n# I. Introduction\n## High-throughput atomistic calculations\n## Workflow packages for ab initio databases\n## Reproducibility, modular steps, and user-focused interfaces","[{\"question\":\"What problem does wfl target in atomistic machine learning workflows?\",\"answer\":\"wfl targets the need to manage many diverse calculations required for MLIP fitting and related atomistic simulations, where task types and parallelization requirements differ from typical ab initio workflow managers.\"},{\"question\":\"How does the paper position existing workflow packages compared with MLIP needs?\",\"answer\":\"Existing packages largely focus on building ab initio property databases with structured, reproducibility- and provenance-oriented workflows, but MLIP communities often require additional strategies for parallelization and efficient local or remote execution.\"},{\"question\":\"What roles do wfl and ExPyRe play in the proposed solution?\",\"answer\":\"wfl provides an atomistic simulation and MLIP fitting workflow management capability, while ExPyRe supplies Python-based remote execution support to enable efficient execution of diverse procedures.\"}]","wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows | 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