[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124140-en":3,"doc-seo-124140-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},124140,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting Young’s Moduli of Nanopillars by Interpretable Machine Learning - Bachelor’s thesis","Understanding the relationship between atomic structure and mechanical properties is a central challenge in materials science. This thesis uses interpretable machine learning to study how atomic structure relates to Young’s moduli of polycrystalline nanopillars. A convolutional neural network is trained to predict Young’s moduli from atomic-structure representations, including grain boundary atom densities, lattice orientations, and raw atom positions. Prediction accuracy is poor for grain boundary densities, while lattice orientations and raw atom positions perform strongly. Grad-CAM is applied to interpret the model by linking activation maps with locally computed Young’s modulus fields via lattice orientation, revealing a strong positive correlation.","Teemu Koivisto  \nPREDICTING YOUNG’S MODULI OF  \nNANOPILLARS BY INTERPRETABLE MACHINE LEARNING  \nBachelor’s thesis  \nFaculty of Engineering and Natural Sciences Examiner: Prof. Lasse Laurson  \nMay 2024  \ni  \nABSTRACT  \nTeemu Koivisto: Predicting Young’s Moduli of Nanopillars by Interpretable Machine Learning  \nBachelor’s thesis Tampere University Science and Engineering May 2024  \nUnderstanding the relationship between the atomic structure and mechanical properties of materials is a fundamental problem in materials science. The field of machine learning has seen rapid progress in recent years, leading to its application in a wide variety of fields, including materials science. Neural networks have been successfully employed to predict mechanical properties of materials from their atomic structure. However, a weakness of neural networks is that they are\"black box models\", as their decision process is not easily comprehensible by humans. Therefore, methods for interpreting neural networks are actively researched. In the context of materials science, interpretable neural networks have the potential to not only accurately model the relationship between the atomic structure and mechanical properties of materials, but also to deepen human understanding of the relationship.  \nThis thesis studies the relationship between the atomic structure and Young’s moduli of polycrystalline nanopillars by interpretable machine learning. The goal of this thesis is to train a convolutional neural network (CNN) to predict the Young’s moduli of nanopillars from their atomic structures, and to interpret the neural network.  \nA dataset is created in order to train the CNN. A set of polycrystalline tantalum nanopillars are numerically generated, and their Young’s moduli are measured by deforming them in molecular dynamics simulations. As an input to the CNN, three different fields are extracted from the atomic structures of the nanopillars, which represent grain boundary atom densities, lattice orientations, and raw atom positions. CNNs are trained to predict the Young’s moduli from each of these representations of the initial state. It is found that the prediction accuracy is poor with the grain boundary atom densities, indicating that the grain boundaries contain minimal information about the Young’s modulus. In contrast, a very high prediction accuracy is achieved using lattice orientations and even slightly higher with raw atom positions.  \nGradient-weighted class activation mapping (Grad-CAM) is used to interpret the CNNs. This method generates fields that represent how different areas of a CNN’s inputs influence the produced outputs. Grad-CAM fields are extracted from different convolutional layers of the CNNs for the input pillars. Local Young’s modulus fields are calculated based on local lattice orientations, and the correlation between the Grad-CAM fields and local Young’s modulus fields is studied. It is found that there is a strong positive correlation between the two fields.  \nIn conclusion, the first goal of training a neural network to predict the Young’s moduli of nanopillars from their atomic structures was successfully met. The second goal of interpreting the neural network was achieved to some degree, but further research could be conducted to make the interpretation more comprehensive.  \nKeywords: Young’s modulus, nanopillar, interpretable machine learning, convolutional neural network, molecular dynamics  \nThe originality of this thesis has been checked using the Turnitin OriginalityCheck service.  \nii  \nTIIVISTELMÄ  \nTeemu Koivisto: Nanopilarien Youngin moduulien ennustaminen tulkittavalla koneoppimisella  \nKandidaatintyö Tampereen yliopisto Teknis-luonnontieteellinen Toukokuu 2024  \nMateriaalien atomirakenteen ja mekaanisten ominaisuuksien suhteen ymmärtäminen on tärkeä ongelma materiaalitekniikassa. Koneoppimisen nopea kehitys viime vuosina on johtanut sen hyödyntämiseen useilla eri aloilla, mukaan lukien materiaalitekniikassa. Ne","cbCaijbXqcb9qpH8","https://ap.wps.com/l/cbCaijbXqcb9qpH8","pdf",7824816,1,43,"English","en",105,"# Abstract\n## Objective and approach\n## Dataset creation and model inputs\n## Prediction results\n## Model interpretation with Grad-CAM\n## Conclusions","[{\"question\":\"What is the main goal of this thesis?\",\"answer\":\"To train a convolutional neural network to predict Young’s moduli of nanopillars from atomic structures and to interpret how the model makes its predictions.\"},{\"question\":\"Which input representations were tested for predicting Young’s modulus?\",\"answer\":\"Grain boundary atom densities, lattice orientations, and raw atom positions extracted from the nanopillar atomic structures.\"},{\"question\":\"How does the thesis interpret the trained neural network?\",\"answer\":\"It uses Grad-CAM to generate fields showing which input regions influence the output, and then correlates these fields with locally computed Young’s modulus fields.\"}]","Predicting Young’s Moduli of Nanopillars by Interpretable Machine Learning - Bachelor’s thesis | PDF",1785820666,108,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-youngs-moduli-of-nanopillars-by-interpretable-machine-learning-bachelors-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-youngs-moduli-of-nanopillars-by-interpretable-machine-learning-bachelors-thesis/124140/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this thesis?","Question",{"text":75,"@type":76},"To train a convolutional neural network to predict Young’s moduli of nanopillars from atomic structures and to interpret how the model makes its predictions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which input representations were tested for predicting Young’s modulus?",{"text":80,"@type":76},"Grain boundary atom densities, lattice orientations, and raw atom positions extracted from the nanopillar atomic structures.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis interpret the trained neural network?",{"text":84,"@type":76},"It uses Grad-CAM to generate fields showing which input regions influence the output, and then correlates these fields with locally computed Young’s modulus fields.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]