{"title":"BenchML: An extensible pipelining framework for benchmarking representations of materials and molecules at scale","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","issue":"4","department":[{"_id":"BiCh"}],"external_id":{"isi":["000886534000001"]},"_id":"12128","citation":{"ama":"Poelking C, Faber FA, Cheng B. BenchML: An extensible pipelining framework for benchmarking representations of materials and molecules at scale. <i>Machine Learning: Science and Technology</i>. 2022;3(4). doi:<a href=\"https://doi.org/10.1088/2632-2153/ac4d11\">10.1088/2632-2153/ac4d11</a>","ista":"Poelking C, Faber FA, Cheng B. 2022. BenchML: An extensible pipelining framework for benchmarking representations of materials and molecules at scale. Machine Learning: Science and Technology. 3(4), 040501.","ieee":"C. Poelking, F. A. Faber, and B. Cheng, “BenchML: An extensible pipelining framework for benchmarking representations of materials and molecules at scale,” <i>Machine Learning: Science and Technology</i>, vol. 3, no. 4. IOP Publishing, 2022.","short":"C. Poelking, F.A. Faber, B. Cheng, Machine Learning: Science and Technology 3 (2022).","chicago":"Poelking, Carl, Felix A Faber, and Bingqing Cheng. “BenchML: An Extensible Pipelining Framework for Benchmarking Representations of Materials and Molecules at Scale.” <i>Machine Learning: Science and Technology</i>. IOP Publishing, 2022. <a href=\"https://doi.org/10.1088/2632-2153/ac4d11\">https://doi.org/10.1088/2632-2153/ac4d11</a>.","apa":"Poelking, C., Faber, F. A., &#38; Cheng, B. (2022). BenchML: An extensible pipelining framework for benchmarking representations of materials and molecules at scale. <i>Machine Learning: Science and Technology</i>. IOP Publishing. <a href=\"https://doi.org/10.1088/2632-2153/ac4d11\">https://doi.org/10.1088/2632-2153/ac4d11</a>","mla":"Poelking, Carl, et al. “BenchML: An Extensible Pipelining Framework for Benchmarking Representations of Materials and Molecules at Scale.” <i>Machine Learning: Science and Technology</i>, vol. 3, no. 4, 040501, IOP Publishing, 2022, doi:<a href=\"https://doi.org/10.1088/2632-2153/ac4d11\">10.1088/2632-2153/ac4d11</a>."},"month":"11","article_type":"original","year":"2022","scopus_import":"1","ddc":["000"],"volume":3,"publication":"Machine Learning: Science and Technology","oa_version":"Published Version","status":"public","author":[{"first_name":"Carl","last_name":"Poelking","full_name":"Poelking, Carl"},{"full_name":"Faber, Felix A","last_name":"Faber","first_name":"Felix A"},{"first_name":"Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","last_name":"Cheng","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632"}],"type":"journal_article","keyword":["Artificial Intelligence","Human-Computer Interaction","Software"],"intvolume":"         3","publication_identifier":{"issn":["2632-2153"]},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"related_material":{"link":[{"relation":"software","url":"https://github.com/capoe/benchml"}]},"date_updated":"2023-08-04T08:49:53Z","date_published":"2022-11-17T00:00:00Z","publication_status":"published","quality_controlled":"1","article_number":"040501","has_accepted_license":"1","day":"17","article_processing_charge":"No","isi":1,"abstract":[{"lang":"eng","text":"We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guiding principle underlying the benchmarking approach is to evaluate raw descriptor performance by limiting model complexity to simple regression schemes while enforcing best ML practices, allowing for unbiased hyperparameter optimization, and assessing learning progress through learning curves along series of synchronized train-test splits. The resulting models are intended as baselines that can inform future method development, in addition to indicating how easily a given dataset can be learnt. Through a comparative analysis of the training outcome across a diverse set of physicochemical, topological and geometric representations, we glean insight into the relative merits of these representations as well as their interrelatedness."}],"acknowledgement":"C P acknowledges funding from Astex through the Sustaining Innovation Program under the Milner Consortium. B C acknowledges resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service funded by EPSRC Tier-2 capital Grant EP/P020259/1. F A F acknowledges funding from the Swiss National Science Foundation (Grant No. P2BSP2_191736). ","date_created":"2023-01-12T12:02:21Z","language":[{"iso":"eng"}],"oa":1,"publisher":"IOP Publishing","file":[{"checksum":"8930d4ad6ed9b47358c6f1a68666adb6","access_level":"open_access","relation":"main_file","file_name":"2022_MachLearning_Poelking.pdf","content_type":"application/pdf","success":1,"file_id":"12343","file_size":13814559,"date_created":"2023-01-23T10:42:04Z","date_updated":"2023-01-23T10:42:04Z","creator":"dernst"}],"doi":"10.1088/2632-2153/ac4d11","file_date_updated":"2023-01-23T10:42:04Z"}