An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems  

作　　者：Yuwei Mao Mahmudul Hasan Arindam Paul Vishu Gupta Kamal Choudhary Francesca Tavazza Wei-keng Liao Alok Choudhary Pinar Acar Ankit Agrawal 

机构地区：[1]Department of Electrical and Computer Engineering,Northwestern University,Evanston,IL,USA [2]Virginia Tech,Blacksburg,VA,USA [3]Materials Measurement Laboratory,National Institute of Standards and Technology,Gaithersburg,MD 20899,USA [4]Theiss Research,La Jolla,CA 92037,USA

出　　处：《npj Computational Materials》2023年第1期1217-1226,共10页计算材料学（英文）

基　　金：This work was supported primarily by National Science Foundation(NSF)CMMI awards 2053929/2053840;Partial support from NIST award 70NANB19H005 and DOE awards DE-SC0019358,DE-SC0021399 is also acknowledged.

摘　　要：Materials design aims to identify the material features that provide optimal properties for various engineering applications,such as aerospace,automotive,and naval.One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties.This paper proposes an end-to-end artificial intelligence(AI)-driven microstructure optimization framework for elastic properties of materials.In this work,the microstructure is represented by the Orientation Distribution Function(ODF)that determines the volume densities of crystallographic orientations.The framework was evaluated on two crystal systems,cubic and hexagonal,for Titanium(Ti)in Joint Automated Repository for Various Integrated Simulations(JARVIS)database and is expected to be widely applicable for materials with multiple crystal systems.The proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time.

关 键 词：microstructure CUBIC CRYSTAL 

分 类 号：O7[理学—晶体学] TP18[自动化与计算机技术—控制理论与控制工程]