{"data":{"id":"10.4121/63aa9122-8e07-4211-a57b-53a61efd5fc6","type":"dois","attributes":{"doi":"10.4121/63aa9122-8e07-4211-a57b-53a61efd5fc6","prefix":"10.4121","suffix":"63aa9122-8e07-4211-a57b-53a61efd5fc6","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Thakolkaran, Prakash","nameType":"Personal","givenName":"Prakash","familyName":"Thakolkaran","nameIdentifiers":[{"nameIdentifier":"0000-0002-5836-6588","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]}],"titles":[{"title":"Data and code underlying the PhD thesis: Data-driven methods to design, learn, and interpret complex materials across scales"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2025,"subjects":[{"subject":"Artificial Intelligence and Image Processing","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0801"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Mechanical Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0913"},{"subject":"FOS: Mechanical engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Materials Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0912"},{"subject":"FOS: Materials engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"09"},{"subject":"Information and Computing Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"08"},{"subject":"Machine Learning"},{"subject":"Materials Modelling"},{"subject":"Materials Design"},{"subject":"Interpretability"},{"subject":"Deep Learning"},{"subject":"Neural Networks"},{"subject":"Kolmogorov-Arnold Networks"}],"contributors":[{"name":"TU Delft, Faculty Of Mechanical Engineering, Department Of Materials Science And Engineering","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2025-04-16","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/63aa9122-8e07-4211-a57b-53a61efd5fc6.v1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[".csv, .py, .pth, .txt, .md, .pdf, .m"],"version":"1","rightsList":[{"rights":"MIT License","rightsUri":"https://opensource.org/licenses/MIT","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"mit","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This repository contains code and data related to the underlying PhD thesis: Data-driven methods to design, learn, and interpret complex materials across scales. The repository is divided into the individual codes and datasets of each chapter. \u003cstrong\u003eChapter 2\u003c/strong\u003e explores the inverse design of 2D metamaterials for elastic properties, utilizing machine learning techniques to optimize material structure and performance. \u003cstrong\u003eChapter 3\u003c/strong\u003e focuses on learning hyperelastic material models without relying on stress data, employing data-driven approaches to predict material behavior under large strains. \u003cstrong\u003eChapter 4\u003c/strong\u003e extends this by developing interpretable hyperelastic material models, ensuring both accuracy and physical consistency without stress data. \u003cstrong\u003eChapter 5\u003c/strong\u003e explores the inverse design of 3D metamaterials under finite strains and applies novel ML frameworks to design these complex material structures. \u003cstrong\u003eChapter 6\u003c/strong\u003e investigates the use of deep learning to uncover key predictors of thermal conductivity in covalent organic frameworks (COFs) and reveals new insights into the relationship between molecular structure and thermal transport. \u003cstrong\u003eChapter 7\u003c/strong\u003e introduces a graph grammar-based approach for generating novel polymers in data-scarce settings, thus combines computational design with minimal data.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://data.4tu.nl/datasets/63aa9122-8e07-4211-a57b-53a61efd5fc6","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2025-04-16T09:12:52.000Z","registered":"2025-04-16T09:12:56.000Z","published":"2025","updated":"2025-04-16T09:12:57.000Z"},"relationships":{"client":{"data":{"id":"delft.data4tu","type":"clients"}},"provider":{"data":{"id":"scpr","type":"providers"}},"media":{"data":{"id":"10.4121/63aa9122-8e07-4211-a57b-53a61efd5fc6","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[{"id":"10.4121/63aa9122-8e07-4211-a57b-53a61efd5fc6.v1","type":"dois"}]}}}}