biostudies-literatureMao YNIST DOCNational Institute of Standards and TechnologyDOENational Science Foundation24074https://www.ebi.ac.uk/biostudies/studies/S-EPMC12228741biostudies-literatureUnknown15(1)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 material processing simulations, yielding faster results with limited error rates (< 0.3% for both the elastic matrix C and the compliance matrix S), making it a promising tool for the expedited design of microstructures with tailored properties.Scientific reportsAn AI framework for time series microstructure prediction from processing parameters.PMC12228741205392970NANB24H136DE-SC0021399Choudhary ABillah MMKilic MNTGupta VAcar PLee CSChakrabarty SWang KAgrawal ALi YLiao WKHasan MMao YfalseAn AI framework for time series microstructure prediction from processing parameters.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 material processing simulations, yielding faster results with limited error rates (< 0.3% for both the elastic matrix C and the compliance matrix S), making it a promising tool for the expedited design of microstructures with tailored properties.2025-01-01T00:00:00Z2025 Jul2026-06-03T07:39:36.256Z2026-04-26T03:09:40.689ZS-EPMC122287414061794110.1038/s41598-025-06894-x