{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:28:58Z","timestamp":1762342138336,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"U. S. Department of Energy","award":["DE-AC05-000R22725"],"award-info":[{"award-number":["DE-AC05-000R22725"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,7]]},"DOI":"10.1145\/3605731.3608931","type":"proceedings-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T23:50:00Z","timestamp":1694130600000},"page":"172-179","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Multiobjective Hyperparameter Optimization for Deep Learning Interatomic Potential Training Using NSGA-II"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1020-531X","authenticated-orcid":false,"given":"Mark","family":"Coletti","sequence":"first","affiliation":[{"name":"Oak Ridge National Laboratory, USA"}]},{"given":"Ada","family":"Sedova","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, USA"}]},{"given":"Rajni","family":"Chahal","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, USA"}]},{"given":"Luke","family":"Gibson","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, USA"}]},{"given":"Santanu","family":"Roy","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, USA"}]},{"given":"Vyacheslav","family":"Bryantsev","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29939-5"},{"key":"e_1_3_2_1_2_1","volume-title":"Random search for hyper-parameter optimization.Journal of machine learning research 13, 2","author":"Bergstra James","year":"2012","unstructured":"James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization.Journal of machine learning research 13, 2 (2012)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2017.8297018"},{"key":"e_1_3_2_1_4_1","volume-title":"Rank-based Non-dominated Sorting. arXiv preprint arXiv:2203.13654","author":"Burlacu Bogdan","year":"2022","unstructured":"Bogdan Burlacu. 2022. Rank-based Non-dominated Sorting. arXiv preprint arXiv:2203.13654 (2022)."},{"key":"e_1_3_2_1_5_1","volume-title":"Transferable Deep Learning Potential Reveals Intermediate-Range Ordering Effects in LiF\u2013NaF\u2013ZrF4 Molten Salt. JACS Au","author":"Chahal Rajni","year":"2022","unstructured":"Rajni Chahal, Santanu Roy, Martin Brehm, Shubhojit Banerjee, Vyacheslav Bryantsev, and Stephen\u00a0T Lam. 2022. Transferable Deep Learning Potential Reveals Intermediate-Range Ordering Effects in LiF\u2013NaF\u2013ZrF4 Molten Salt. JACS Au (2022)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/DLS49591.2019.00010"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3449726.3459573"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377929.3398147"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCSE.2021.3083216"},{"key":"e_1_3_2_1_10_1","volume-title":"Dask: Library for dynamic task scheduling. https:\/\/dask.org","author":"Team Dask Development","year":"2016","unstructured":"Dask Development Team. 2016. Dask: Library for dynamic task scheduling. https:\/\/dask.org"},{"key":"e_1_3_2_1_11_1","volume-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","author":"Deb Kalyanmoy","year":"2002","unstructured":"Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation 6, 2 (2002), 182\u2013197."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3002502"},{"key":"e_1_3_2_1_13_1","volume-title":"MPI: a message passing interface standard: version 3.1","author":"Passing\u00a0Interface Forum Message","year":"2015","unstructured":"Message Passing\u00a0Interface Forum. 2015. MPI: a message passing interface standard: version 3.1; Message Passing Interface Forum, June 4, 2015. University of Tennessee."},{"key":"e_1_3_2_1_14_1","unstructured":"D. Frenkel and B. Smit. 2001. Understanding Molecular Simulation: From Algorithms to Applications. Elsevier Science. https:\/\/books.google.com\/books?id=5qTzldS9ROIC"},{"key":"e_1_3_2_1_15_1","volume-title":"large minibatch SGD: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677","author":"Goyal Priya","year":"2017","unstructured":"Priya Goyal, Piotr Doll\u00e1r, Ross Girshick, 2017. Accurate, large minibatch SGD: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677 (2017)."},{"volume-title":"Handbook of evolutionary computation","author":"G\u00fcnter Rudolph","key":"e_1_3_2_1_16_1","unstructured":"Rudolph G\u00fcnter. 1997. Handbook of evolutionary computation. CRC Press, Chapter Evolution strategies, B1.3:2."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/3433701.3433707"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.100.014105"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3019245"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1063\/5.0007045"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3506695"},{"key":"e_1_3_2_1_22_1","volume-title":"Summit: America\u2019s Newest and Smartest Supercomputer. https:\/\/www.olcf.ornl.gov\/for-users\/system-user-guides\/summit\/summit-user-guide\/#system-overview","author":"Leadership Computing Facility ORNL","year":"2019","unstructured":"ORNL Leadership Computing Facility. 2019. Summit: America\u2019s Newest and Smartest Supercomputer. https:\/\/www.olcf.ornl.gov\/for-users\/system-user-guides\/summit\/summit-user-guide\/#system-overview"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.1c06742"},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the 20th UK Workshop on Computational Intelligence","author":"Scott O.","year":"2021","unstructured":"Eric\u00a0O. Scott, Mark Coletti, Catherine\u00a0D. Schuman, 2021. Avoiding Excess Computation in Asynchronous Evolutionary Algorithms. In Proceedings of the 20th UK Workshop on Computational Intelligence. Aberystwyth University, (to be printed)."},{"key":"e_1_3_2_1_25_1","volume-title":"Proceedings of 2022 Cray User Group Meeting","author":"Sedova Ada","year":"2022","unstructured":"Ada Sedova, Russ Davidson, Mathieu Taillefumier, and Wael Elwasif. 2022. HPC Molecular Simulation Tries Out a New GPU: Experiences on Early AMD Test Systems for the Frontier Supercomputer. In Proceedings of 2022 Cray User Group Meeting, Monterey, CA, USA, May 2022."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","unstructured":"Ada Sedova John\u00a0D. Eblen Reuben Budiardja Arnold Tharrington and Jeremy\u00a0C. Smith. 2018. High-Performance Molecular Dynamics Simulation for Biological and Materials Sciences: Challenges of Performance Portability. In 2018 IEEE\/ACM International Workshop on Performance Portability and Productivity in HPC (P3HPC). 1\u201313. https:\/\/doi.org\/10.1109\/P3HPC.2018.00004","DOI":"10.1109\/P3HPC.2018.00004"},{"key":"e_1_3_2_1_27_1","volume-title":"Horovod: fast and easy distributed deep learning in TensorFlow. arXiv preprint arXiv:1802.05799","author":"Sergeev Alexander","year":"2018","unstructured":"Alexander Sergeev and Mike\u00a0Del Balso. 2018. Horovod: fast and easy distributed deep learning in TensorFlow. arXiv preprint arXiv:1802.05799 (2018)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.0c01111"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-32027-3"},{"key":"e_1_3_2_1_30_1","volume-title":"2018. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics.Computer Physics Communications 228","author":"Wang Han","year":"2018","unstructured":"Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E.2018. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics.Computer Physics Communications 228 (2018), 178\u2013184. https:\/\/github.com\/deepmodeling\/deepmd-kit"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2018.03.016"},{"key":"e_1_3_2_1_32_1","volume-title":"Deep potentials for materials science. Materials Futures","author":"Wen Tongqi","year":"2022","unstructured":"Tongqi Wen, Linfeng Zhang, Han Wang, E Weinan, and David\u00a0J Srolovitz. 2022. Deep potentials for materials science. Materials Futures (2022)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpcb.0c11618"}],"event":{"name":"ICPP-W 2023: 52nd International Conference on Parallel Processing Workshops","acronym":"ICPP-W 2023","location":"Salt Lake City UT USA"},"container-title":["Proceedings of the 52nd International Conference on Parallel Processing Workshops"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3605731.3608931","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3605731.3608931","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:10Z","timestamp":1750178770000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3605731.3608931"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,7]]},"references-count":33,"alternative-id":["10.1145\/3605731.3608931","10.1145\/3605731"],"URL":"https:\/\/doi.org\/10.1145\/3605731.3608931","relation":{},"subject":[],"published":{"date-parts":[[2023,8,7]]},"assertion":[{"value":"2023-09-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}