[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118623-en":3,"doc-seo-118623-105":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},118623,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Guest Editorial - Special Topic on Software for Atomistic Machine Learning","Guest Editorial surveys contributions to the Journal of Chemical Physics Special Topic on Software for Atomistic Machine Learning, focusing on how publication pathways support software research in a landscape dominated by search. The editorial summarizes 28 invited and contributed articles, highlighting that 18 (64%) address machine-learning interatomic potentials (MLIPs). It outlines integration efforts into molecular dynamics and quantum chemistry codes and reviews software based on neural-network MLIPs, including GPUMD, CHARMM, CASTEP, and Behler–Parrinello-inspired architectures.","Guest Editorial: Special Topic on Software for Atomistic Machine Learning  \nMatthias Rupp,1, a) Emine K¨u¸c¨ukbenli,2, b) and G´abor Cs´anyi3, c)  \n1) Luxembourg Institute of Science and Technology, L-4362 Esch-sur-Alzette, Luxembourg  \n2) John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA, and, Nvidia Corporation, Santa Clara, CA, USA  \n3) Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ, United Kingdom (Submitted to The Journal of Chemical Physics)  \nA survey of the contributions to the Special Topic on Software for Atomistic Machine Learning.  \narXiv :2406 . 19750v1 [physics .chem-ph] 28 Jun 2024  \nI. Introduction  \nWelcome to the Journal of Chemical Physics’ Special Topic on Software for Atomistic Machine Learning. For some years now, search engines have been dominating our online experience and have essentially overtaken libraries, whether physical or digital, as the means to find information we are looking for. Most readers of an original research article find it by citation or direct search, and not by browsing journal volumes. Given this, one might wonder what the utility of a Special Topic issue of a scientific journal might be.  \nHowever, publishing papers on scientific software has traditionally been somewhat neglected, with few go-to journals for publishing, such as the Journal of Open Source Software or Computer Physics Communications. Typical published software papers tend to discuss relatively mature software packages. In this context, the Journal of Chemical Physics’ initiative 1 to support software publications is especially welcome. Given the huge activity currently taking place across many sub-fields and communities in new software development for atomistic machine learning (ML), this landscape is changing fast. Arguably, many papers in this Special Topic issue might not have been written if it were not for the impetus provided by this Special Topic issue.  \nBeyond their individual value regarding specific software packages, these papers as a collection provide a snapshot at this moment in time of the kinds of tools that people use and the goals they set themselves and achieve for the software implementations of their methods. Table I presents an overview of the 28 invited and contributed articles.2–29 Of these, 18 (64%) deal directly with machine-learning interatomic potentials (MLIPs) . The other 10 articles cover a broad range of subjects, ranging from sampling to data set repositories and workflows.  \nIn the following, we give an overview of these contributions.  \na) [mrupp@mrupp.info](mrupp@mrupp.info)  \nb)[ekucukbenli@nvidia.com](ekucukbenli@nvidia.com)  \nc)[gc121@cam.ac.uk](gc121@cam.ac.uk)  \nII. Contributions  \nSince their beginnings in the 1980s and 1990s, MLIPshave undergone tremendous development and now constitute a highly active field of research. Some modern MLIPs can predict forces with an accuracy close to the underlying ab-initio reference method for atomistic systems with many chemical elements and millions of atoms while still providing orders of magnitude of acceleration. These capabilities have increasingly enabled scientific applications using MLIPs that would not otherwise have been possible.  \nConsequently, there is an trend to directly integrate MLIPs into molecular dynamics codes. In this Special Topic, four contributions describe the integration of (a) neuro-evolution potentials into GPUMD (Graphics Processing Units Molecular Dynamics), including improved featurization, GPU code, active learning, and supporting Python packages gpyumd, calorine, and pynep;3 (b) PhysNet into CHARMM (Chemistry at HARvard Macromolecular Mechanics) via a new MLpot extension of the pyCHARMM interface, with para-chloro-phenol as an example; 12 (c) general MLIPs into CASTEP (CAmbridge Serial Total Energy Package), including active learning, using the example of a GAP/SOAP model; 16 (d) ephemeral data-derived potentials with","cbCaisRb0dxHrEQl","https://ap.wps.com/l/cbCaisRb0dxHrEQl","pdf",316805,1,5,"English","en",105,"# I. Introduction\n# II. Contributions\n## Integration of MLIPs into simulation codes\n## Behler–Parrinello-inspired neural-network MLIPs","[{\"question\":\"What is the purpose of this Guest Editorial in the Journal of Chemical Physics special topic?\",\"answer\":\"It surveys the contributions to the Special Topic on Software for Atomistic Machine Learning and explains why software publication in this area is valuable and timely.\"},{\"question\":\"How many articles are covered, and what fraction focus on MLIPs?\",\"answer\":\"The editorial summarizes 28 invited and contributed articles, with 18 (64%) dealing directly with machine-learning interatomic potentials (MLIPs).\"},{\"question\":\"Which simulation-code integrations are highlighted for MLIPs in this special topic?\",\"answer\":\"It highlights integrations into GPUMD, CHARMM (via a new MLpot extension of pyCHARMM), and CASTEP (including an example with a GAP/SOAP model), alongside related approaches such as AIRSS-derived potentials.\"}]","Guest Editorial - 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