{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T10:28:44Z","timestamp":1770460124218,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T00:00:00Z","timestamp":1662508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Center of Research Innovation and Excellence of University of Thessaly, Special Account for Research Grants of University of Thessaly","award":["5600.03.08.03"],"award-info":[{"award-number":["5600.03.08.03"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,9,7]]},"DOI":"10.1145\/3549737.3549802","type":"proceedings-article","created":{"date-parts":[[2022,9,9]],"date-time":"2022-09-09T16:29:59Z","timestamp":1662740999000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Calculating material properties with purely data-driven methods"],"prefix":"10.1145","author":[{"given":"Konstantinos","family":"Papastamatiou","sequence":"first","affiliation":[{"name":"Department of Physics\/ School of Science\/ Condensed Matter Physics Laboratory, University of Thessaly, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Filippos","family":"Sofos","sequence":"additional","affiliation":[{"name":"Department of Physics\/ School of Science\/ Condensed Matter Physics Laboratory, University of Thessaly, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Theodoros E.","family":"Karakasidis","sequence":"additional","affiliation":[{"name":"Department of Physics\/ School of Science\/ Condensed Matter Physics Laboratory, University of Thessaly, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,9,9]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Computer Simulation of Liquids","author":"Allen Michael P","unstructured":"Michael P Allen and Dominic J Tildesley . 2017. Computer Simulation of Liquids : Second Edition (2 nd ed.). Oxford University Press , Oxford . DOI:https:\/\/doi.org\/10.1093\/oso\/9780198803195.001.0001 10.1093\/oso Michael P Allen and Dominic J Tildesley. 2017. Computer Simulation of Liquids: Second Edition (2nd ed.). Oxford University Press, Oxford. DOI:https:\/\/doi.org\/10.1093\/oso\/9780198803195.001.0001","edition":"2"},{"key":"e_1_3_2_1_2_1","volume-title":"Machine learning prediction of self-diffusion in Lennard-Jones fluids. The Journal of Chemical Physics","author":"Allers Joshua P","year":"2020","unstructured":"Joshua P Allers , Jacob A Harvey , Fernando H Garzon , and Todd M Alam . 2020. Machine learning prediction of self-diffusion in Lennard-Jones fluids. The Journal of Chemical Physics ( 2020 ), 12. DOI:https:\/\/doi.org\/10.1063\/5.0011512 10.1063\/5.0011512 Joshua P Allers, Jacob A Harvey, Fernando H Garzon, and Todd M Alam. 2020. Machine learning prediction of self-diffusion in Lennard-Jones fluids. The Journal of Chemical Physics (2020), 12. DOI:https:\/\/doi.org\/10.1063\/5.0011512"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1021\/jp2039898"},{"key":"e_1_3_2_1_4_1","unstructured":"S. Chapman T.G. Cowling D. Burnett and C. Cercignani. 1990. The Mathematical Theory of Non-uniform Gases: An Account of the Kinetic Theory of Viscosity Thermal Conduction and Diffusion in Gases. Cambridge University Press. Retrieved from https:\/\/books.google.gr\/books?id=Cbp5JP2OTrwC  S. Chapman T.G. Cowling D. Burnett and C. Cercignani. 1990. The Mathematical Theory of Non-uniform Gases: An Account of the Kinetic Theory of Viscosity Thermal Conduction and Diffusion in Gases. Cambridge University Press. Retrieved from https:\/\/books.google.gr\/books?id=Cbp5JP2OTrwC"},{"key":"e_1_3_2_1_5_1","volume-title":"Retrieved","author":"Cranmer Miles","year":"2020","unstructured":"Miles Cranmer , Alvaro Sanchez-Gonzalez , Peter Battaglia , Rui Xu , Kyle Cranmer , David Spergel , and Shirley Ho . 2020 . Discovering Symbolic Models from Deep Learning with Inductive Biases. arXiv:2006.11287 [astro-ph, physics:physics, stat] (November 2020) . Retrieved April 12, 2021 from http:\/\/arxiv.org\/abs\/2006.11287 Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, and Shirley Ho. 2020. Discovering Symbolic Models from Deep Learning with Inductive Biases. arXiv:2006.11287 [astro-ph, physics:physics, stat] (November 2020). Retrieved April 12, 2021 from http:\/\/arxiv.org\/abs\/2006.11287"},{"key":"e_1_3_2_1_6_1","volume-title":"Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm. npj Comput Mater 6, 1 (September","author":"Dunn Alexander","year":"2020","unstructured":"Alexander Dunn , Qi Wang , Alex Ganose , Daniel Dopp , and Anubhav Jain . 2020. Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm. npj Comput Mater 6, 1 (September 2020 ), 1\u201310. DOI:https:\/\/doi.org\/10.1038\/s41524-020-00406-3 10.1038\/s41524-020-00406-3 Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, and Anubhav Jain. 2020. Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm. npj Comput Mater 6, 1 (September 2020), 1\u201310. DOI:https:\/\/doi.org\/10.1038\/s41524-020-00406-3"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.2307\/2346830"},{"key":"e_1_3_2_1_8_1","volume-title":"Genetic programming: on the programming of computers by means of natural selection","author":"Koza John R.","unstructured":"John R. Koza . 1992. Genetic programming: on the programming of computers by means of natural selection . MIT Press , Cambridge, Mass . John R. Koza. 1992. Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, Mass."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3007748.3007773"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1063\/5.0082147"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-23479-0"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1165893"},{"key":"e_1_3_2_1_13_1","volume-title":"Rousseeuw","author":"Schubert Erich","year":"2021","unstructured":"Erich Schubert and Peter J . Rousseeuw . 2021 . Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms. Information Systems 101, (November 2021), 101804. DOI:https:\/\/doi.org\/10.1016\/j.is.2021.101804 10.1016\/j.is.2021.101804 Erich Schubert and Peter J. Rousseeuw. 2021. Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms. Information Systems 101, (November 2021), 101804. DOI:https:\/\/doi.org\/10.1016\/j.is.2021.101804"},{"key":"e_1_3_2_1_14_1","volume-title":"Smits and Mark Kotanchek","author":"Guido","year":"2005","unstructured":"Guido F. Smits and Mark Kotanchek . 2005 . Pareto-Front Exploitation in Symbolic Regression. In Genetic Programming Theory and Practice II, Una-May O'Reilly, Tina Yu, Rick Riolo and Bill Worzel (eds.). Springer US , Boston, MA, 283\u2013299. DOI:https:\/\/doi.org\/10.1007\/0-387-23254-0_17 10.1007\/0-387-23254-0_17 Guido F. Smits and Mark Kotanchek. 2005. Pareto-Front Exploitation in Symbolic Regression. In Genetic Programming Theory and Practice II, Una-May O'Reilly, Tina Yu, Rick Riolo and Bill Worzel (eds.). Springer US, Boston, MA, 283\u2013299. DOI:https:\/\/doi.org\/10.1007\/0-387-23254-0_17"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1063\/5.0096669"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijheatmasstransfer.2008.07.022"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00231-013-1152-9"},{"key":"e_1_3_2_1_18_1","first-page":"3","article-title":"Current Trends in Fluid Research in the Era of Artificial Intelligence","volume":"7","author":"Sofos Filippos","year":"2022","unstructured":"Filippos Sofos , Christos Stavrogiannis , Kalliopi K. Exarchou-Kouveli , Daniel Akabua , George Charilas , and Theodoros E. Karakasidis . 2022 . Current Trends in Fluid Research in the Era of Artificial Intelligence : A Review. Fluids 7 , 3 (March 2022), 116. DOI:https:\/\/doi.org\/10.3390\/fluids7030116 10.3390\/fluids7030116 Filippos Sofos, Christos Stavrogiannis, Kalliopi K. Exarchou-Kouveli, Daniel Akabua, George Charilas, and Theodoros E. Karakasidis. 2022. Current Trends in Fluid Research in the Era of Artificial Intelligence: A Review. Fluids 7, 3 (March 2022), 116. DOI:https:\/\/doi.org\/10.3390\/fluids7030116","journal-title":"A Review. Fluids"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1080\/00268978700102371"},{"key":"e_1_3_2_1_20_1","volume-title":"Thermophysical Properties of the Lennard-Jones Fluid: Database and Data Assessment. Journal of Chemical Information and Modeling October","author":"Stephan Simon","year":"2019","unstructured":"Simon Stephan , Monika Thol , Jadran Vrabec , and Hans Hasse . 2019. Thermophysical Properties of the Lennard-Jones Fluid: Database and Data Assessment. Journal of Chemical Information and Modeling October ( 2019 ), 4248\u20134265. DOI:https:\/\/doi.org\/10.1021\/acs.jcim.9b00620 10.1021\/acs.jcim.9b00620 Simon Stephan, Monika Thol, Jadran Vrabec, and Hans Hasse. 2019. Thermophysical Properties of the Lennard-Jones Fluid: Database and Data Assessment. Journal of Chemical Information and Modeling October (2019), 4248\u20134265. DOI:https:\/\/doi.org\/10.1021\/acs.jcim.9b00620"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmultiphaseflow.2020.103533"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/nphys2371"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aay2631"},{"key":"e_1_3_2_1_24_1","volume-title":"Modeling assembly bias with machine learning and symbolic regression. arXiv:2012.00111 [astro-ph, physics:physics]","author":"Wadekar Digvijay","year":"2020","unstructured":"Digvijay Wadekar , Francisco Villaescusa-Navarro , Shirley Ho , and Laurence Perreault-Levasseur . 2020. Modeling assembly bias with machine learning and symbolic regression. arXiv:2012.00111 [astro-ph, physics:physics] ( 2020 ). Digvijay Wadekar, Francisco Villaescusa-Navarro, Shirley Ho, and Laurence Perreault-Levasseur. 2020. Modeling assembly bias with machine learning and symbolic regression. arXiv:2012.00111 [astro-ph, physics:physics] (2020)."},{"key":"e_1_3_2_1_25_1","volume-title":"Material Properties: Measurement and Data","author":"Wakeham William","year":"2007","unstructured":"William Wakeham , Marc Assael , Abraham Marmur , Jo\u00ebl Coninck , Terry Blake , Stephanus Theron , and Eyal Zussman . 2007 . Material Properties: Measurement and Data . In Springer Handbook of Experimental Fluid Mechanics, Cameron Tropea , Alexander L. Yarin and John F. Foss (eds.). Springer Berlin Heidelberg , Berlin, Heidelberg, 85\u2013177. DOI:https:\/\/doi.org\/10.1007\/978-3-540-30299-5_3 10.1007\/978-3-540-30299-5_3 William Wakeham, Marc Assael, Abraham Marmur, Jo\u00ebl Coninck, Terry Blake, Stephanus Theron, and Eyal Zussman. 2007. Material Properties: Measurement and Data. In Springer Handbook of Experimental Fluid Mechanics, Cameron Tropea, Alexander L. Yarin and John F. Foss (eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 85\u2013177. DOI:https:\/\/doi.org\/10.1007\/978-3-540-30299-5_3"},{"key":"e_1_3_2_1_26_1","volume-title":"Research on K-Value Selection Method of K-Means Clustering Algorithm. J 2, 2 (June","author":"Yuan Chunhui","year":"2019","unstructured":"Chunhui Yuan and Haitao Yang . 2019. Research on K-Value Selection Method of K-Means Clustering Algorithm. J 2, 2 (June 2019 ), 226\u2013235. DOI:https:\/\/doi.org\/10.3390\/j2020016 10.3390\/j2020016 Chunhui Yuan and Haitao Yang. 2019. Research on K-Value Selection Method of K-Means Clustering Algorithm. J 2, 2 (June 2019), 226\u2013235. DOI:https:\/\/doi.org\/10.3390\/j2020016"},{"key":"#cr-split#-e_1_3_2_1_27_1.1","doi-asserted-by":"crossref","unstructured":"Yu Zhu Xiaohua Lu Jian Zhou Yanru Wang and Jun Shi. 2002. Prediction of diffusion coefficients for gas liquid and supercritical fluid: application to pure real fluids and infinite dilute binary solutions based on the simulation of Lennard-Jones fluid. Fluid Phase Equilibria 194-197 (March 2002) 1141-1159. DOI:https:\/\/doi.org\/10.1016\/S0378-3812(01)00669-0 10.1016\/S0378-3812(01)00669-0","DOI":"10.1016\/S0378-3812(01)00669-0"},{"key":"#cr-split#-e_1_3_2_1_27_1.2","doi-asserted-by":"crossref","unstructured":"Yu Zhu Xiaohua Lu Jian Zhou Yanru Wang and Jun Shi. 2002. Prediction of diffusion coefficients for gas liquid and supercritical fluid: application to pure real fluids and infinite dilute binary solutions based on the simulation of Lennard-Jones fluid. Fluid Phase Equilibria 194-197 (March 2002) 1141-1159. DOI:https:\/\/doi.org\/10.1016\/S0378-3812(01)00669-0","DOI":"10.1016\/S0378-3812(01)00669-0"}],"event":{"name":"SETN 2022: 12th Hellenic Conference on Artificial Intelligence","location":"Corfu Greece","acronym":"SETN 2022"},"container-title":["Proceedings of the 12th Hellenic Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3549737.3549802","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3549737.3549802","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:55Z","timestamp":1750183795000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3549737.3549802"}},"subtitle":["From clusters to symbolic expressions"],"short-title":[],"issued":{"date-parts":[[2022,9,7]]},"references-count":28,"alternative-id":["10.1145\/3549737.3549802","10.1145\/3549737"],"URL":"https:\/\/doi.org\/10.1145\/3549737.3549802","relation":{},"subject":[],"published":{"date-parts":[[2022,9,7]]},"assertion":[{"value":"2022-09-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}