Papers citing An informatics approach to transformation temperatures of NiTi-based shape memory alloys
Adaptive machine learning for efficient materials design
MRS Bulletin · 2020
Prasanna V. Balachandran
Read original paper ↗ ·Machine learning assisted materials design and discovery for rechargeable batteries
Energy storage materials · 2020
Yue Liu, Biru Guo, Xinxin Zou, Yajie Li, Siqi Shi
Read original paper ↗ ·Role of uncertainty estimation in accelerating materials development via active learning
Journal of Applied Physics · 2020
Yuan Tian, Ruihao Yuan, Dezhen Xue, Yumei Zhou, Xiangdong Ding, Jun Feng Sun, Turab Lookman
Read original paper ↗ ·Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization
The Journal of Chemical Physics · 2020
Zachary del Rosario, Matthias Rupp, Yoolhee Kim, Erin Antono, Julia Ling
Read original paper ↗ ·Cluster-formula-embedded machine learning for design of multicomponent β-Ti alloys with low Young’s modulus
npj Computational Materials · 2020
Fei Yang, Zhen Li, Qing Wang, Beibei Jiang, Biaojie Yan, Pengcheng Zhang, Wei Xu, Chuang Dong, Peter Kaehuei Liaw
Read original paper ↗ ·Rapid evaluation method for anisotropic growth of WS2 monolayers by combining machine learning algorithms and kinetic Monte Carlo simulation data
Computational Materials Science · 2020
Yuanfeng Xia, Lixiang Wu, Gaofeng Wang
Read original paper ↗ ·Accelerated design of L12-strengthened Co-base superalloys based on machine learning of experimental data
Materials & Design · 2020
Jinxin Yu, Chenglei Wang, Chenglei Wang, Yuechao Chen, Cuiping Wang, Cuiping Wang, Xingjun Liu
Read original paper ↗ ·Machine learning in additive manufacturing: State-of-the-art and perspectives
Additive manufacturing · 2020
Chengcheng Wang, Xipeng Tan, Shu Beng Tor, C.S. Lim
Read original paper ↗ ·An Informatic Approach to Predict the Mechanical Properties of Aluminum Alloys using Machine Learning Techniques
International Conference on Smart Electronics and Communication (ICOSEC) · 2020
M Aruna Devi, C.P.S. Prakash, Rahul Pandappa Chinnannavar, Venugopal Prasanna Joshi, Rohit Shankar Palada, Ravut Dixit
Read original paper ↗ ·Artificial intelligence for photonics and photonic materials
Reports on Progress in Physics · 2020
Davide Piccinotti, Kevin F. MacDonald, Simon A. Gregory, Ian J. Youngs, Nikolay I. Zheludev
Read original paper ↗ ·Knowledge-Based Descriptor for the Compositional Dependence of the Phase Transition in BaTiO3-Based Ferroelectrics
ACS Applied Materials & Interfaces · 2020
Ruihao Yuan, Deqing Xue, Deqing Xue, Dezhen Xue, Dezhen Xue, Jinshan Li, Xiangdong Ding, Jun Sun, Turab Lookman
Read original paper ↗ ·Transformation Temperature Predictions Through Computational Intelligence for NiTi-Based Shape Memory Alloys
Shape Memory and Superelasticity · 2020
Yun Zhang, Xiaojie Xu
Read original paper ↗ ·A screening strategy for hot forging combining high-throughput forging experiment and machine learning
Materials Research Express · 2020
Zhiren Sun, Kaikun Wang
Read original paper ↗ ·Prediction of the NiTi shape memory alloy composition with the best corrosion resistance for dental applications utilizing artificial intelligence
Materials Chemistry and Physics · 2020
Alireza Nazarahari, Demircan Canadinç
Read original paper ↗ ·Data analytics approach to predict the hardness of copper matrix composites
Metallurgical and Materials Engineering · 2020
Somesh Kr. Bhattacharya, Ryoji Sahara, D. Božić, Jovana Ružić
Read original paper ↗ ·Machine learning-aided design of aluminum alloys with high performance
Materials Today Communications · 2020
Umer Masood Chaudry, Kotiba Hamad, Tamer Abuhmed
Read original paper ↗ ·Determining Multi‐Component Phase Diagrams with Desired Characteristics Using Active Learning
Advanced Science · 2020
Yuan Tian, Ruihao Yuan, Dezhen Xue, Yumei Zhou, Yunfan Wang, Xiangdong Ding, Jun Feng Sun, Turab Lookman
Read original paper ↗ ·Evaluating yield strength of Ni-based superalloys via high throughput experiment and machine learning
Journal of Micromechanics and Molecular Physics · 2020
Feng Liu, Zexin Wang, Zi Wang, Zijun Qin, Zihang Li, Liang Jiang, Lan Huang, Liming Tan, Yong Liu
Read original paper ↗ ·Physics-informed machine learning for composition – process – property design: Shape memory alloy demonstration
Applied Materials Today · 2020
Sen Liu, Branden B. Kappes, Behnam Amin-Ahmadi, Othmane Benafan, Xiaoli Zhang, Aaron P. Stebner
Read original paper ↗ ·Data science applications for predictive maintenance and materials science in context to Industry 4.0
Materials Today Proceedings · 2021
Sufiyan Sajid, Abid Haleem, Shashi Bahl, Mohd Javaid, Tarun Goyal, Manoj Mittal
Read original paper ↗ ·Deep Learning Approach for Chemistry and Processing History Prediction From Materials Microstructure: Application to Spinodal Decomposition
SSRN Electronic Journal · 2021
Amir Abbas Kazemzadeh Farizhandi, Omar Betancourt, Mahmood Mamivand
Read original paper ↗ ·Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest
Springer eBooks · 2021
Baldur Steingrimsson, Xuesong Fan, Anand P. Kulkarni, Michael C. Gao, Peter Kaehuei Liaw
Read original paper ↗ ·Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys
Acta Materialia · 2021
Hongtao Zhang, Huadong Fu, Shuaicheng Zhu, Wei Yong, Jianxin Xie
Read original paper ↗ ·Efficient estimation of material property curves and surfaces via active learning
Physical Review Materials · 2021
Yuan Tian, Dezhen Xue, Ruihao Yuan, Yumei Zhou, Xiangdong Ding, Jun Sun, Turab Lookman
Read original paper ↗ ·Accelerated Development of High-Strength Magnesium Alloys by Machine Learning
Metallurgical and Materials Transactions A · 2021
Yanwei Liu, Leyun Wang, Huan Zhang, Gaoming Zhu, Jie Wang, Yuhui Zhang, Xiaoqin Zeng
Read original paper ↗ ·Data Science, Machine Learning and Artificial Intelligence Applied to Metals and Alloys Research: Past, Present, and Future
Elsevier eBooks · 2021
Raymundo Arróyave
Read original paper ↗ ·Machine learning in materials science: From explainable predictions to autonomous design
Computational Materials Science · 2021
Ghanshyam Pilania
Read original paper ↗ ·Machine learning of phases and mechanical properties in complex concentrated alloys
Journal of Material Science and Technology · 2021
Jie Xiong, San‐Qiang Shi, Tong‐Yi Zhang
Read original paper ↗ ·Machine learning identified materials descriptors for ferroelectricity
Acta Materialia · 2021
Jingjin He, Junjie Li, Chuanbao Liu, Changxin Wang, Yan Zhang, Cheng Wen, Dezhen Xue, Jiangli Cao, Yanjing Su, Lijie J. Qiao, Yang Bai
Read original paper ↗ ·Efficient sampling for decision making in materials discovery*
Chinese Physics B · 2021
Yuan Tian, Turab Lookman, Dezhen Xue
Read original paper ↗ ·Determination of glass forming ability of bulk metallic glasses based on machine learning
Computational Materials Science · 2021
Peng Li, Zhilin Long, Mingshengzi Zhao
Read original paper ↗ ·Machine learning assisted screening of non-rare-earth elements for Mg alloys with low stacking fault energy
Computational Materials Science · 2021
M. Wang, Hulei Yu, Y. Chen, Mingxin Huang
Read original paper ↗ ·A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys
Scientific Reports · 2021
Jin-Woong Lee, Chaewon Park, Byung Do Lee, Joonseo Park, Nam Hoon Goo, Kee‐Sun Sohn
Read original paper ↗ ·Machine learning for alloys
Nature Reviews Materials · 2021
Gus L. W. Hart, Tim Mueller, Cormac Toher, Stefano Curtarolo A
Read original paper ↗ ·Designing highly ductile magnesium alloys: current status and future challenges
Critical reviews in solid state and materials sciences/CRC critical reviews in solid state and materials sciences · 2021
Umer Masood Chaudry, Sravya Tekumalla, Manoj Kumar Gupta, Tea‐Sung Jun, Kotiba Hamad
Read original paper ↗ ·Machine learning assisted empirical formula augmentation
Materials & Design · 2021
Bin Xiong, Xinpeng Zhao, Yunfeng Hu, Haiyou Huang, Yang Liu, Yanjing Su
Read original paper ↗ ·Prediction of two-phase composite microstructure properties through deep learning of reduced dimensional structure-response data
Composites Part B Engineering · 2021
Ganapathi Ammasai Sengodan
Read original paper ↗ ·Characterization of the Convoluted 3D Intermetallic Phases in a Recycled Al Alloy by Synchrotron X-ray Tomography and Machine Learning
Acta Metallurgica Sinica (English Letters) · 2021
Zhenhao Li, Ling Qin, Baisong Guo, Junping Yuan, Zhiguo Zhang, Wei Hao Li, Jiawei Mi
Read original paper ↗ ·Age-hardening behavior guided by the multi-objective evolutionary algorithm and machine learning
Journal of Alloys and Compounds · 2021
Russlan Jaafreh, Umer Masood Chaudry, Kotiba Hamad, Tamer Abuhmed
Read original paper ↗ ·ASMADA—A tool for automatic analysis of shape memory alloy thermal cycling data under constant stress
Smart Materials and Structures · 2021
Matthew C. Kuner, Anargyros A. Karakalas, Dimitris C. Lagoudas
Read original paper ↗ ·DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions
Venue not supplied by source · 2021
Sterling G. Baird, Tran Diep, Taylor D. Sparks
Read original paper ↗ ·DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions
Venue not supplied by source · 2021
Sterling G. Baird, Tran Diep, Taylor D. Sparks
Read original paper ↗ ·Prediction of Aluminum Alloys Composition for Industrial Requirement Using Data Analysis Techniques
Advances in sustainability science and technology · 2021
M. Arunadevi, C.P.S. Prakash, Venugopal Prasanna Joshi, Rohit Shankar Palada, Ravut Dixit, Rahul Pandappa Chinnannavar
Read original paper ↗ ·DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions
Venue not supplied by source · 2021
Sterling G. Baird, Tran Diep, Taylor D. Sparks
Read original paper ↗ ·Computational materials design: Composition optimization to develop novel Ni-based single crystal superalloys
Computational Materials Science · 2021
Bin Xu, Haiqing Yin, Xue Feng Jiang, Cong Zhang, Ruijie Zhang, Yongwei Wang, Xuanhui Qu
Read original paper ↗ ·An Interpretable Boosting-Based Predictive Model for Transformation Temperatures of Shape Memory Alloys
Computational Materials Science · 2022
Sina Hossein Zadeh, Amir Behbahanian, John Broucek, Mingzhou Fan, Guillermo Vazquez, Mohammad Noroozi, William Trehern, Xiaoning Qian, İbrahim Karaman, Raymundo Arróyave
Read original paper ↗ ·Artificial Intelligence based Approach for Rapid Material Discovery: From Chemical Synthesis to Quantum Materials
Venue not supplied by source · 2022
Robert S. Tempke
Read original paper ↗ ·DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions
Digital Discovery · 2022
Sterling G. Baird, Tran Diep, Taylor D. Sparks
Read original paper ↗ ·Estimating the performance of a material in its service space via Bayesian active learning: a case study of the damping capacity of Mg alloys
Journal of Materials Informatics · 2022
Bofeng Shi, Yumei Zhou, Daqing Fang, Yuan Tian, Xiangdong Ding, Jun Sun, Turab Lookman, Dezhen Xue
Read original paper ↗ ·NiTi shape memory alloys: properties
Elsevier eBooks · 2022
Soroush Parvizi, Seyed Mahdi Hashemi, Sanaz Moein
Read original paper ↗ ·