{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Mao Y"],"funding":["NIST DOC","National Institute of Standards and Technology","DOE","National Science Foundation"],"pagination":["24074"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12228741"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["In this study, we present an artificial intelligence (AI)-driven framework for predicting the microstructural texture of polycrystalline materials after a specific deformation process. The microstructural texture is defined in terms of the orientation distribution function (ODF) which indicates the volume density of crystal orientations. Our approach leverages an encoder-decoder model with Long Short-Term Memory (LSTM) layers to model the relationship between processing conditions and material properties. As a case study, we apply our framework to copper, generating a dataset of 3125 unique processing parameter combinations and their corresponding ODF vectors. The resulting predictions enable the calculation of homogenized properties. Our AI-driven framework outperforms traditional materia"],"journal":["Scientific reports"],"pubmed_title":["An AI framework for time series microstructure prediction from processing parameters."],"pmcid":["PMC12228741"],"funding_grant_id":["2053929","70NANB24H136","DE-SC0021399"],"pubmed_authors":["Choudhary A","Billah MM","Kilic MNT","Gupta V","Acar P","Lee CS","Chakrabarty S","Wang K","Agrawal A","Li Y","Liao WK","Hasan M","Mao Y"],"additional_accession":[]},"is_claimable":false,"name":"An AI framework for time series microstructure prediction from processing parameters.","description":"In this study, we present an artificial intelligence (AI)-driven framework for predicting the microstructural texture of polycrystalline materials after a specific deformation process. The microstructural texture is defined in terms of the orientation distribution function (ODF) which indicates the volume density of crystal orientations. Our approach leverages an encoder-decoder model with Long Short-Term Memory (LSTM) layers to model the relationship between processing conditions and material properties. As a case study, we apply our framework to copper, generating a dataset of 3125 unique processing parameter combinations and their corresponding ODF vectors. The resulting predictions enable the calculation of homogenized properties. Our AI-driven framework outperforms traditional materia","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jul","modification":"2026-06-03T07:39:36.256Z","creation":"2026-04-26T03:09:40.689Z"},"accession":"S-EPMC12228741","cross_references":{"pubmed":["40617941"],"doi":["10.1038/s41598-025-06894-x"]}}