← Назад к работе
Работы, на которые ссылается эта работа
Работ: 85
Работа: <scp>LAX</scp> phases: A family of novel stable layered materials, informatics‐based discovery
Accelerating materials property predictions using machine learning
Ghanshyam Pilania, Chenchen Wang, Xun Jiang +2
Статья2013Цитирований: 2ABIMatminer: An open source toolkit for materials data mining
Logan Ward, Alexander Dunn, Alireza Faghaninia +13
Статья2018Цитирований: 2ABIIn- and Out-of-Plane Ordered MAX Phases and Their MXene Derivatives
Johanna Rosén, Martin Dahlqvist, Quanzheng Tao +1
Глава2019Цитирований: 2ABIThe role of Hume-Rothery's rules play in the MAX phases formability
Yiming Zhang, Zeyu Mao, Qi Han +5
Статья2020Цитирований: 2ABIArtificial intelligence and machine learning in design of mechanical materials
Kai Guo, Zhenze Yang, Chi‐Hua Yu +1
Обзорная статья2020Цитирований: 2ABIMachine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries
Chade Lv, Xin Zhou, Lixiang Zhong +8
Обзорная статья2021Цитирований: 2ABIRoom‐Temperature Carbide‐Derived Carbon Synthesis by Electrochemical Etching of MAX Phases
Maria R. Lukatskaya, Joseph Halim, Boris Dyatkin +4
Статья2014Цитирований: 2ABIMaximizing the mechanical performance of Ti3AlC2-based MAX phases with aid of machine learning
Xingjun Duan, Zhi Fang, Tao Yang +5
Статья2022Цитирований: 2ABISynthesis of (Ti1−xWx)3SiC2 MAX phase solid solution and its high-temperature oxidation performance
Lielin Wang, Qing‐Yun Chen, Tao Yang +3
Статья2022Цитирований: 2ABIStructure maps for MAX phases formability revisited
Yiming Zhang, Yongjia Xu, Qing Huang +5
Статья2023Цитирований: 2ABI