[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122696-en":3,"doc-seo-122696-105":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},122696,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",6,"Technology","wfl Python Toolkit for Creating Machine Learning Interatomic Potentials and Related Atomistic Simulation Workflows","Predictive atomistic simulations support data-intensive high-throughput studies, but manual execution becomes impractical as task volumes grow. Existing workflow managers often target ab initio pipelines with heavy computations, emphasizing provenance and reproducibility, while machine-learning interatomic potential (MLIP) communities face different needs: more diverse task types, sizes, and counts, plus efficient local or remote parallel execution. This work presents wfl for workflow management and ExPyRe for Python remote execution to enable versatile ASE-based MLIP fitting automation.","wfl Python Toolkit for Creating Machine Learning Interatomic Potentialsand Related Atomistic Simulation Workflows  \nElena Gelžinyte˙*,1 Simon Wengert,2 Tamás K. Stenczel,1 Hendrik H. Heenen,2 Karsten Reuter,2 Gábor Csányi,1 and Noam Bernstein3  \n1) Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom  \n2)Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany  \n3) Center for Materials Physics and Technology, U. S. Naval Research Laboratory Code 6393, 4555 Overlook Ave SW, Washington, DC 20375, United States of America  \n(*Electronic mail: [eg475@cam.ac.uk](eg475@cam.ac.uk))  \n(Dated: 1 August 2023)  \nPredictive 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 procedures 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.  \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 be performed completely independently of therest. Some examples include single point calculations, geometry optimisation, spectra and similar prediction of large databases of atomistic structures, high-throughput screening (e.g. random structure search), or generating reference data for fitting machine-learning or conventional interatomic potentials. Due to the throughput enabled by modern High Performance Computing (HPC) it is no longer practical for these calculations be launched and monitored manually, and a number of packages to manage such workflows have recently been developed.  \nMost of the workflow packages focus on building ab initio material property databases3,10,11,13,14,19,27 . The frameworks define workflows to calculate certain structural and electronic properties, for example band structures, spectra or dielectric constants, and are themselves made up of modular steps, e.g. geometry optimisation, structure perturbations, and single point calculations. These packages provide a consistent way to build and extend the large databases, e.g. Materials Project 12 , Open Quantum Materials Database31 , Aflow9 , and pay particular attention to reproducibility and data provenance. To go in hand with large projects, most of the exist-  \ning packages tend to provide a relatively structured and userfocused interface for (re-)running the workflows, analysing and querying the results. Such packa","cbCaifoUza98mLKK","https://ap.wps.com/l/cbCaifoUza98mLKK","pdf",579146,1,12,"English","en",105,"# Introduction\n## High-throughput atomistic simulations and workflow bottlenecks\n## Existing workflow packages and their focus\n## MLIP fitting workflows and the gap\n## Goals of wfl and ExPyRe\n## Workflow comparison overview","[{\"question\":\"Why are new workflow management tools needed for MLIP fitting compared with existing ab initio workflow packages?\",\"answer\":\"MLIP workflows involve more diverse and numerous tasks, requiring additional parallelization strategies and efficient local or remote execution beyond what existing packages primarily target.\"},{\"question\":\"What roles do wfl and ExPyRe play in the proposed workflow solution?\",\"answer\":\"wfl provides atomistic simulation and MLIP fitting workflow management, while ExPyRe enables Python-based remote execution to support efficient parallel runs.\"},{\"question\":\"How do the packages enable automation of MLIP fitting procedures?\",\"answer\":\"They provide a low-level developer-oriented framework that supports constructing high-level user-friendly functionality, and wfl already includes capabilities to automate MLIP fitting workflows.\"}]","wfl Python Toolkit for Creating Machine Learning Interatomic Potentials and Related Atomistic Simulation Workflows | 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are new workflow management tools needed for MLIP fitting compared with existing ab initio workflow packages?","Question",{"text":75,"@type":76},"MLIP workflows involve more diverse and numerous tasks, requiring additional parallelization strategies and efficient local or remote execution beyond what existing packages primarily target.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What roles do wfl and ExPyRe play in the proposed workflow solution?",{"text":80,"@type":76},"wfl provides atomistic simulation and MLIP fitting workflow management, while ExPyRe enables Python-based remote execution to support efficient parallel runs.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the packages enable automation of MLIP fitting procedures?",{"text":84,"@type":76},"They provide a low-level developer-oriented framework that supports constructing high-level user-friendly functionality, and wfl already includes capabilities to automate MLIP fitting 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