Papers citing Steel design based on a large language model
LLM-driven hybrid truss-lattice design via structural gene recombination
International Journal of Mechanical Sciences · 2026
Siyang Cao, Han Meng, Tian Jian Lu
Read original paper ↗ ·TriboBERT: A pre-training language model tailored for tribology
Tribology International · 2026
Wenhao He, Zhibin Lu
Read original paper ↗ ·Knowledge-guided descriptors for multi-property prediction and candidate screening of superalloys
Materials Today Communications · 2026
Weiren Wang, Xue Jiang, Yanjing Su
Read original paper ↗ ·LLM‐Powered AI Agents in Materials Science: From Autonomous Design to Autonomous Experimentation
Advanced Functional Materials · 2026
Tongbo Jiang, Zhengyang Zhang, Mingshuo Nie, Dexin Zhu, Erli Meng, Hong‐Hui Wu, Xinping Mao
Read original paper ↗ ·Knowledge‐Constrained Multi‐Objective Reinforcement Learning for Simultaneous Optimization of Printability and Mechanical Properties in Additively Manufactured Superalloys
Materials Genome Engineering Advances · 2026
Pei Liu, Xueting Ren, ShiBo Wei, Shuai Guan, Guohao Liu, Teng An, Haiyou Huang, Bi Zhongnan
Read original paper ↗ ·Large language models in materials science: mechanistic understanding, structure–property reasoning and future opportunities
Progress in Materials Science · 2026
Haoyang Xie, Zini Yan, Danyu Luo, Lu Chen, Kai Yu, Jian Hui, Turab Lookman, Ziyuan Rao, Yang Ren, Xiaoqin Zeng
Read original paper ↗ ·AI-assisted mining and application of wheel-rail tribological information from literature: A case study on wear
Tribology International · 2026
Honghao Wang, Yunfei Jia, Junwei Jin, Shuyue Zhang, Haohao Ding, Qiang Lin, Zhinan Zhang, Zhongrong Zhou, Enrico Meli, Roger Lewis, Wenjian Wang
Read original paper ↗ ·