[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116934-en":3,"doc-seo-116934-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},116934,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Open-Source Machine Learning in Computational Chemistry - Perspective","Computational chemistry is increasingly integrating machine learning concepts and algorithms. This Perspective surveys 179 open-source software projects, each linked to peer-reviewed papers from the last five years, to map the topics studied by machine learning in the field. For every project, the authors summarize its purpose, provide code links, note license types, and state whether training data and models are publicly available. It also identifies widely used Python libraries and offers community suggestions grounded in open data, open source code, and open models.","This article is licensed under CC-BY-NC-ND 4.0  \n[pubs.acs.org/jcim](pubs.acs.org/jcim)  Perspective   \nOpen-Source Machine Learning in Computational Chemistry  \nAlexander Hagg and Karl N. Kirschner *  \n Cite This: J. Chem. Inf. Model. 2023, 63, 4505−4532  \nRead Online  \nACCESS  \n Metrics & More  \n Article Recommendations  \nDownloaded via HOCHSCHULE BONN RHEIN SIEG on July 19, 2024 at 16:06:15 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nABSTRACT: The field of computational chemistry has seen a significant increase in the integration of machine learning conceptsand algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.  \n1. INTRODUCTION  \nCreating models and performing simulations are cornerstones of science. Prior to computers, models were created on paper (e.g., mathematics, diagrams) or physically constructed from material. Modern modeling is done on computers (in silico), allowing one to easily adjust parameters and quickly observe the resulting effects. Today, a plethora of simulation and modeling codes exist, which can be either open or closed to the public. While closed-source software is created by companies for economic reasons, open-source software (OSS) has played an important role in scientific discovery. The OSS philosophy promotes the distribution of code (i.e., tools) and subsequently the natural and computer science knowledge that is embedded within the code. OSS encourages researchers to read the code critically, to understand its mathematical formulations, parameters, and assumptions and the workflow’s logic, and to modify it as desired. Free and open-source software (FOSS), a subcategory of OSS, also demands licensing models that provide a legal framework for the free distribution, use, and development of the code, albeit that commercial usage might still be restricted.  \nThe field of machine learning (ML) has clearly grown, as can be seen by the increased number of research articles published that include it and through the interest shown by the general public. Paraphrasing Sonnenburg et al., OSS benefits the ML field by enabling better reproducibility of scientific results and quicker detection of errors as well as faster, innovative combinations of scientific ideas and their sub-  \nsequent applications to diverse disciplines. 1 To this list are added the benefits of being able to more easily validate the assumptions and approximations made during model building. The very goal and act of making ML algorithms and their trained models open-source has the following three benefits to the field: (1) standardizing interfaces (e.g., adopting specific frameworks), (2) enabling experimentation (e.g., guiding project choices and obtaining alternative perspectives), and (3) community creation (e.g., developer−user interactions and improved educational material).2  \nThe field of computational ch","cbCainCftvFEmCUf","https://ap.wps.com/l/cbCainCftvFEmCUf","pdf",2687221,1,28,"English","en",105,"# Introduction\n## Benefits of open-source for machine learning\n## Impact of OSS and ML in computational chemistry\n## Purpose of the Perspective","[{\"question\":\"What is the main purpose of the Perspective on open-source machine learning in computational chemistry?\",\"answer\":\"It provides an overview of open-source Python-based ML tools available to computational chemistry researchers, supported by a survey of relevant projects and their associated peer-reviewed papers.\"},{\"question\":\"How many open-source software projects are surveyed, and what timeframe do the related papers cover?\",\"answer\":\"The Perspective surveys 179 open-source projects, with corresponding peer-reviewed papers published within the last five years.\"},{\"question\":\"What information is provided for each surveyed project?\",\"answer\":\"Each project includes a short description, a link to the code, the license type, and whether the training data and resulting models are publicly available.\"}]","Open-Source Machine Learning in Computational Chemistry - 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