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Using a prior-data-fitted network, the study achieves near-perfect classification for meteorites, mineral groups, and individual minerals. Regressors, especially KNeighbor, accurately predict hardness, reduced Young’s modulus, and elastic recovery from nanoindentation.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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are nondestructive testing methods important for lunar meteorites?","Question",{"text":63,"@type":64},"Because lunar meteorites are scarce, preserving their scientific value requires methods that avoid damaging the samples while still extracting useful information.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How do the models predict mineralogical and mechanical properties in this study?",{"text":68,"@type":64},"They use machine learning based solely on atomic percentage compositions, with classification models for mineral identification and regressor models for mechanical properties.",{"name":70,"@type":61,"acceptedAnswer":71},"Which regressor model performed particularly well for mechanical property estimation?",{"text":72,"@type":64},"The KNeighbor model is highlighted as providing an outstanding estimate of mechanical properties such as hardness, reduced Young’s modulus, and elastic 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mechanical properties prediction  \nEloy Peña-Asensio a, ⇑, Josep M. Trigo-Rodríguez b,c, Jordi Sort d,e, Jordi Ibáñez-Insa f, Albert Rimola g  \na Department of Aerospace Science and Technology, Politecnico di Milano, Milano 20156, Italy b Institut de Ciències de l’Espai (ICE-CSIC), Campus UAB, 08193 Cerdanyola del Vallès, Spain c Institut d’Estudis Espacials de Catalunya (IEEC), Barcelona 08034, Spain  \nd Departament de Física, Universitat Autònoma de Barcelona, Cerdanyola del Vallès E-08193, Spain e Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona E-08010, Spain fGeosciences Barcelona (GEO3BCN-CSIC), E-08028 Barcelona, Spain  \ng Departament de Química, Universitat Autònoma de Barcelona, Bellaterra 08193, Spain  \n| a r t i c l e i n f o |  | a b s t r a c t |\n| --- | --- | --- |\n| Article history:\u003Cbr>Received 29 April 2024\u003Cbr>Received in revised form 19 July 2024 Accepted 11 August 2024\u003Cbr>Available online 3 September 2024 |  | Amid the scarcity of lunar meteorites and the imperative to preserve their scientific value, nondestructive testing methods are essential. This translates into the application of microscale rock mechanics experiments and scanning electron microscopy for surface composition analysis. This study explores the application of Machine Learning algorithms in predicting the mineralogical and mechanical properties of DHOFAR 1084, JAH 838, and NWA 11444 lunar meteorites based solely on their atomic percentage compositions. Leveraging a prior-data fitted network model, we achieved near-perfect classification scores for meteorites, mineral groups, and individual minerals. The regressor models, notably the KNeighbor model, provided an outstanding estimate of the mechanical properties—previously measured by nanoindentation tests—such as hardness, reduced Young’s modulus, and elastic recovery. Further considerations on the nature and physical properties of the minerals forming these meteorites, including porosity, crystal orientation, or shock degree, are essential for refining predictions. Our findings underscore the potential of Machine Learning in enhancing mineral identification and mechanical property estimation in lunar exploration, which pave the way for new advancements and quick assessments in extraterrestrial mineral mining, processing, and research.\u003Cbr>􀀁 2024 Published by Elsevier B.V. on behalf of China University of Mining & Technology. This is an open access article under the CC BY-NC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)). |\n| Keywords: Meteorites Moon Mineralogy Machine learning\u003Cbr>Mechanical properties |  |  |\n\n1. Introduction  \nThe integration of Machine Learning (ML) technologies into various scientific and engineering disciplines has been met with both acclaim and skepticism. Despite notable success where it has matched or surpassed human performance in diverse material science applications [1], there remains a significant degree of disbelief within both academia and industry regarding the practical utility and impact of these technologies, particularly in specialized fields like mineral processing [2]. This skepticism is largely a product of repeated cycles of hype, characterized by overblown promises followed by underperformance, leading to disillusionment, reduced investment, and slowed research and development.  \n⇑ Corresponding author.  \nE-mail addresses: [eloy.pena@polimi.it](eloy.pena@polimi.it), [eloy.peas@gmail.com](eloy.peas@gmail.com) (E. Peña-Asensio).  \nIn the context of mineral processing, data-based modeling methods have traditionally been employed as ‘‘soft sensors” to predict variables that are either infrequently measured or difficult to measure, using data from variables that are more readily available. Although applications of partial least squares (PLS) methods for predicting elemental composition using reflectanc","cbCaiohdkclw9dwO","https://ap.wps.com/l/cbCaiohdkclw9dwO","pdf",2124034,"English","# Introduction\n## Machine learning in materials science and the mineral processing gap\n## Data-based “soft sensors” and prior neural network approaches\n## Prior studies using XRF and ML for meteorite identification\n## Extensions to extraterrestrial mineral analysis","[{\"question\":\"Why are nondestructive testing methods important for lunar meteorites?\",\"answer\":\"Because lunar meteorites are scarce, preserving their scientific value requires methods that avoid damaging the samples while still extracting useful information.\"},{\"question\":\"How do the models predict mineralogical and mechanical properties in this study?\",\"answer\":\"They use machine learning based solely on atomic percentage compositions, with classification models for mineral identification and regressor models for mechanical properties.\"},{\"question\":\"Which regressor model performed particularly well for mechanical property estimation?\",\"answer\":\"The KNeighbor model is highlighted as providing an outstanding estimate of mechanical properties such as hardness, reduced Young’s modulus, and elastic recovery.\"}]","Machine learning applications on lunar meteorite minerals - From classification to mechanical properties prediction | PDF",25]