Papers citing An informatics approach to transformation temperatures of NiTi-based shape memory alloys
Accelerated design for elastocaloric performance in NiTi-based alloys through machine learning
Journal of Applied Physics · 2022
Xiaohua Tian, Qiu Zhao, Kun Zhang, Hong-Xing Li, Binglun Han, Dingding Shi, Liwen Zhou, Tianyou Ma, Cheng Wang, Qinlong Wen, Changlong Tan
Read original paper ↗ ·Automated pipeline for superalloy data by text mining
npj Computational Materials · 2022
Weiren Wang, Xue Feng Jiang, Shaohan Tian, Pei Liu, Depeng Dang, Yanjing Su, Turab Lookman, Jianxin Xie
Read original paper ↗ ·Global‐Oriented Strategy for Searching Ultrastrength Martensitic Stainless Steels
Advanced Theory and Simulations · 2022
Xiaobing Hu, Jiajun Zhao, Junjie Li, Zhijun Wang, Yi-Ming Chen, Jincheng Wang
Read original paper ↗ ·A multiple loops machine learning framework to predict the properties of WC–Co based cemented carbides
International Journal of Refractory Metals and Hard Materials · 2022
Zehao Guan, Na Li, Weibin Zhang, Junjun Wang, Chuanbin Wang, Qiang Shen, Zhigang Xu, Jian Peng, Yong Du
Read original paper ↗ ·Design of a NiTiHf shape memory alloy with an austenite finish temperature beyond 400 °C utilizing artificial intelligence
Journal of Alloys and Compounds · 2022
A.A. Catal, E. Bedir, Rifat Yilmaz, Demircan Canadinç
Read original paper ↗ ·Magnetic performance oriented composition design of Sm-Co based alloys by machine learning and experimental studies
Computational Materials Science · 2022
Kai Guo, Hao Lü, Zhi Hao Zhao, Fawei Tang, Haibin Wang, Xiaoyan Song
Read original paper ↗ ·Data-driven shape memory alloy discovery using Artificial Intelligence Materials Selection (AIMS) framework
Acta Materialia · 2022
William Trehern, R. Ortiz-Ayala, Kadri Can Atli, Raymundo Arróyave, İbrahim Karaman
Read original paper ↗ ·Landau theory of ferroelastic phase transitions: Application to martensitic phase transformations
Low Temperature Physics · 2022
Oleksandr V. Barabanov, Anna Kosogor
Read original paper ↗ ·A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation
Mathematics · 2022
Umer Masood Chaudry, Russlan Jaafreh, Abdul Malik, Tea‐Sung Jun, Kotiba Hamad, Tamer Abuhmed
Read original paper ↗ ·Deep learning approach for chemistry and processing history prediction from materials microstructure
Scientific Reports · 2022
Amir Abbas Kazemzadeh Farizhandi, Omar Betancourt, Mahmood Mamivand
Read original paper ↗ ·Machine Learning–Assisted Design of Material Properties
Annual Review of Chemical and Biomolecular Engineering · 2022
Sanket Kadulkar, Zachary M. Sherman, Venkat Ganesan, Thomas M. Truskett
Read original paper ↗ ·Recent progress in the machine learning-assisted rational design of alloys
International Journal of Minerals Metallurgy and Materials · 2022
Huadong Fu, Hong‐Tao Zhang, Changsheng Wang, Wei Yong, Jianxin Xie
Read original paper ↗ ·Accelerated discovery of high-performance piezocatalyst in BaTiO3-based ceramics via machine learning
Nano Energy · 2022
Jingjin He, Chengye Yu, Yuxuan Hou, Xiaopo Su, Junjie Li, Chuanbao Liu, Dezhen Xue, Jiangli Cao, Yanjing Su, Lijie J. Qiao, Turab Lookman, Yang Bai
Read original paper ↗ ·Prediction of Oxygen Evolution Activity for NiCoFe Oxide Catalysts via Machine Learning
ACS Omega · 2022
Xue Feng Jiang, Yong Wang, Baorui Jia, Xuanhui Qu, Mingli Qin
Read original paper ↗ ·Screening for shape memory alloys with narrow thermal hysteresis using combined XGBoost and DFT calculation
Computational Materials Science · 2022
Xiaohua Tian, Liwen Zhou, Kun Zhang, Qiu Zhao, Hongxing Li, Dingding Shi, Tianyou Ma, Cheng Wang, Qinlong Wen, Changlong Tan
Read original paper ↗ ·Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning
International Journal of Minerals Metallurgy and Materials · 2022
Hongtao Zhang, Huadong Fu, Yuheng Shen, Jianxin Xie
Read original paper ↗ ·Design of Ni-based turbine disc superalloys with improved yield strength using machine learning
Journal of Materials Science · 2022
Bin Xu, Haiqing Yin, Xue Feng Jiang, Cong Zhang, Ruijie Zhang, Yongwei Wang, Zhenghua Deng, Xuanhui Qu
Read original paper ↗ ·Machine learning-assisted design of biomedical high entropy alloys with low elastic modulus for orthopedic implants
Journal of Materials Science · 2022
Huseyin Can Ozdemir, E. Bedir, Rifat Yilmaz, Mustafa Baris Yagci, Demircan Canadinç
Read original paper ↗ ·Recent progress in nanocrystalline Sm–Co based magnets
Materials Today Chemistry · 2022
Kai Guo, H. Lu, Guojing Xu, D. Liu, H.B. Wang, X.M. Liu, Xiaoyan Song
Read original paper ↗ ·Processing time, temperature, and initial chemical composition prediction from materials microstructure by deep network for multiple inputs and fused data
Materials & Design · 2022
Amir Abbas Kazemzadeh Farizhandi, Mahmood Mamivand
Read original paper ↗ ·Exploration of the oxidation and ablation resistance of ultra-high-temperature ceramic coatings using machine learning
Ceramics International · 2022
Jie Hao, Lihong Gao, Zhuang Ma, Yanbo Liu, Ling Liu, Shizhen Zhu, Weizhi Tian, Xiaoyu Liu, Zhigang Zhou, Alexandr Alexandrovich Rogachev, Hanyang Liu
Read original paper ↗ ·Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning
Journal of Material Science and Technology · 2022
Yimian Chen, Shuize Wang, Jie Xiong, Guilin Wu, Junheng Gao, Yuan Wu, Guoqiang Ma, Hong‐Hui Wu, Xinping Mao
Read original paper ↗ ·Design of super-hard high-entropy ceramics coatings via machine learning
Ceramics International · 2022
Xiaoqian Xu, Xiaobo Wang, Shaoyu Wu, Luchun Yan, Tao Guo, Kewei Gao, Xiaolu Pang, Alex A. Volinsky
Read original paper ↗ ·Establishing reduced-order process-structure linkages from phase field simulations of dendritic grain growth during solidification
Computational Materials Science · 2022
Jiajun Zhao, Junjie Li, Xiaobing Hu, Yujian Wang, Yiming Chen, Feng He, Zhijun Wang, Zhanglong Zhao, Jincheng Wang
Read original paper ↗ ·Using Machine Learning to Predict Oxygen Evolution Activity for Transition Metal Hydroxide Electrocatalysts
ACS Applied Materials & Interfaces · 2022
Xue Feng Jiang, Yong Wang, Baorui Jia, Xuanhui Qu, Mingli Qin
Read original paper ↗ ·Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy
Chemistry - An Asian Journal · 2022
Xiangdong Wang, Tian Lu, Wenyan Zhou, Xiaobo Ji, Wencong Lu, Jiong Yang
Read original paper ↗ ·Machine-learning model for prediction of martensitic transformation temperature in NiMnSn-based ferromagnetic shape memory alloys
Computational Materials Science · 2022
Xiaohua Tian, Dingding Shi, Kun Zhang, Hong-Xing Li, Liwen Zhou, Tianyou Ma, Cheng Hai Wang, Qinlong Wen, Changlong Tan
Read original paper ↗ ·Low-hysteresis shape-memory ceramics designed by multimode modelling
Nature · 2022
Edward L. Pang, Gregory B. Olson, Christopher Allan Schuh
Read original paper ↗ ·Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys
Journal of Materials Science · 2022
Udesh M. H. U. Kankanamge, Johannes Reiner, Xingjun Ma, Santiago Corujeira Gallo, Wei Ping Xu
Read original paper ↗ ·A perspective on Bayesian methods applied to materials discovery and design
MRS Communications · 2022
Raymundo Arróyave, Danial Khatamsaz, Brent Vela, Richard Couperthwaite, Abhilash Molkeri, Prashant Kumar Singh, Duane D. Johnson, Xiaoning Qian, Ankit Srivastava, Douglas Allaire
Read original paper ↗ ·Machine Learning Captures Synthetic Intuitions for Hollow Nanostructures
ACS Applied Nano Materials · 2022
Xue Feng Jiang, Yongzhi Zhao, Jianfang Liu, Baorui Jia, Xuanhui Qu, Mingli Qin
Read original paper ↗ ·Data-driven materials discovery and synthesis using machine learning methods
Elsevier eBooks · 2022
Sterling G. Baird, Marianne Liu, Hasan Muhammad Sayeed, Taylor D. Sparks
Read original paper ↗ ·State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning
Metals · 2022
Upadesh Subedi, Sachin Poudel, Khem Gyanwali, Yuri Amorim Coutinho, G. Matula, Anil Kunwar
Read original paper ↗ ·Machine learning accelerates the materials discovery
Materials Today Communications · 2022
Jiheng Fang, Ming Xie, Xingqun He, Jiming Zhang, Jieqiong Hu, Yongtai Chen, Youcai Yang, Qinglin Jin
Read original paper ↗ ·Deep Learning of Microstructures
Venue not supplied by source · 2022
Amir Abbas Kazemzadeh Farizhandi
Read original paper ↗ ·Data-Driven Study of Shape Memory Behavior of Multi-Component Ni–Ti Alloys in Large Compositional and Processing Space
Shape Memory and Superelasticity · 2022
Shreyas J. Honrao, Othmane Benafan, John W. Lawson
Read original paper ↗ ·Machine learning assisted design of aluminum-lithium alloy with high specific modulus and specific strength
Materials & Design · 2022
Huiyu Li, Xiwu Li, Yanan Li, Wei Xiao, Kai Wen, Zhihui Li, Yongan Zhang, Baiqing Xiong
Read original paper ↗ ·Interpretable machine learning workflow for evaluation of the transformation temperatures of TiZrHfNiCoCu high entropy shape memory alloys
Materials & Design · 2022
Shiyu He, Yanming Wang, Zhengyang Zhang, Fei Xiao, Shungui Zuo, Ying Zhou, Xiaorong Cai, Xuejun Jin
Read original paper ↗ ·Accelerated discovery of cost-effective Nd–Fe–B magnets through adaptive learning
Journal of Materials Chemistry A · 2023
Jie Chen, Jian Liu, Minjuan Zhang, Zhanji Dong, Zhongjie Peng, Xinyi Ji, Mei Liu, Lanting T. Zhang, Anqi Zhang, Hong Min Zhu
Read original paper ↗ ·Digital Model for Rapid Prediction of Die Forging Force and Forming Control of Aluminum Alloy Aviation Components
SSRN Electronic Journal · 2023
Hao Hu, Fan Zhao, Daoxiang Wu, Wei Yong, Zhengan Wang, Jianxin Xie
Read original paper ↗ ·Discovery of Novel Photocatalysts Using Machine Learning Approach
Springer eBooks · 2023
G. Sudha Priyanga, Gaurav Pransu, Harshita Krishna, Tiju Thomas
Read original paper ↗ ·Effect of Cu Content on the Martensitic Transformation and Shape Memory Behaviors in Ti31.5hf15zr5ni48.5-Xcux Alloys
SSRN Electronic Journal · 2023
Jianbo Pang, Pengfei Dang, Jin Tian, Lei Zhang, Yumei Zhou, Xiangdong Ding, Jun Sun, Dezhen Xue
Read original paper ↗ ·Molecular dynamics simulation of the porosity effect on transformation mechanism of nanocrystalline porous NiTi shape memory alloy
Materials Today Communications · 2023
Bingfei Liu, Zhifan Li, Wenzhao Li, Yaxuan Pan, Wen‐Ping Wu
Read original paper ↗ ·Experimental Feedback to Improve the Extrapolation of Machine Learning: Application to Design of PM Cu–Al–Fe–Ni Alloys
Transactions of the Indian Institute of Metals · 2023
Bin Xu, Haiqing Yin, Ruijie Zhang, Zhenghua Deng, Wei Li
Read original paper ↗ ·Exploring the relationship between lattice distortion and phase stability in a multi-principal element alloy system based on machine learning method
Computational Materials Science · 2023
Jiaxin Huang, Wei Fang, Congcong Xue, Tiexu Peng, Haoyang Yu, Jia Li, Liying Sun, Xinbo He, Baoxi Liu, Yong Gang Yang, Fuxing Yin
Read original paper ↗ ·Design Strategy for Art Copper Alloys’ Colors Through Machine Learning and Oxidation Treatment
JOM · 2023
Shuang Zhou, Qian Lei, Jie Yin, Yuxin Liang, Qianye Ren, Can Dai, Zhou Li
Read original paper ↗ ·Accelerated design of lead-free high-performance piezoelectric ceramics with high accuracy via machine learning
Journal of Advanced Ceramics · 2023
Wei Gu, Bin Yang, Dengfeng Li, Xunzhong Shang, Zhiyong Zhou, Jinming Guo
Read original paper ↗ ·Improved material descriptors for bulk modulus in intermetallic compounds via machine learning
Rare Metals · 2023
Dexin Zhu, Kunming Pan, Yuan Wu, Xiaoye Zhou, Xiang‐Yue Li, Yongpeng Ren, Sairu Shi, Hua Yu, Shizhong Wei, Hong‐Hui Wu, Xusheng Yang
Read original paper ↗ ·Machine learning superalloy microchemistry and creep strength from physical descriptors
Computational Materials Science · 2023
Patrick Taylor, G. J. Conduit
Read original paper ↗ ·Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel
Science China Technological Sciences · 2023
Chunlei Shang, Chuanjun Wang, Hong‐Hui Wu, Wenyue Liu, Yimian Chen, Guangfei Pan, Shuize Wang, Guilin Wu, Junheng Gao, Haitao Zhao, Chaolei Zhang, Xinping Mao
Read original paper ↗ ·