文献类型：期刊文献

英文题名：Active Learning Assisted High-Throughput Phase-Field Design of Linear-Superelastic NiTi Alloys with Controllable Modulus Driven by Temperature Gradients

作者：Xu, Tao[1]; Wang, Chunyu[2]; Zhu, Yuquan[3]; Wang, Yu[1]; Yan, Yabin[2]; Wang, Jie[4,5]; Shimada, Takahiro[1]; Kitamura, Takayuki[1]

机构：[1] Department of Mechanical Engineering and Science, Kyoto University, Nishikyo-ku, Kyoto, 615-8540, Japan; [2] Key Laboratory of Pressure Systems and Safety Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [3] Materials Genome Institute, Shanghai University, Shanghai, 200444, China; [4] Department of Engineering Mechanics, School of Aeronautics, Astronautics Zhejiang University, Hangzhou, 310027, China; [5] Zhejiang Laboratory, Zhejiang, Hangzhou, 311100, China

年份：2023

外文期刊名：SSRN

收录：EI(收录号：20230078126)

语种：英文

外文关键词：Binary alloys - Degrees of freedom (mechanics) - Engineering education - Machine learning - Shape-memory alloy - Thermal gradients - Titanium alloys

摘要：Engineering martensitic transformation (MT) with exceptional and controllable properties is essential for the innovative application of shape memory alloys (SMAs) in advanced technologies. Herein, we combine high-throughput (HTP) phase-field simulations and machine learning approaches to propose the concept of 'temperature-controlled mechanics' and demonstrate that outstanding mechanical properties integrating ultra-low modulus, linear superelasticity, and hysteresis-free could be designed in NiTi alloys under temperature gradient environments. These nontrivial mechanical properties originate from continuous variations of the critical stress for the MT, which contributes to gradual and continuous MT rather than a sharp first-order transition as that in common SMAs. An active learning workflow based on uncertainty sampling is employed to guide phase-field simulations to efficiently clarify and optimize the temperature environment for different NiTi alloys with the desired properties. Furthermore, SISSO (Sure Independence Screening and Sparsifying Operator) algorithm is conducted on the datasets from the HTP simulations to establish an explicit expression for the Young’s modulus, which is verified by additional phase field simulations and is instructive for the inverse design of the temperature field. The present study not only provides fundamental insights to the temperature gradient effects on the MTs and overall mechanical properties, but also offers a promising computational approach for developing advanced materials with extraordinary properties. ? 2023, The Authors. All rights reserved.