从小数据建模到大语言模型选:材料智能设计的双策略框架
Yeyong Yu1, Jie Xiong2, Xing Wu1,3,4
1School of Computer Engineering & Science, Shanghai University, Shanghai, 200444, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|October 4, 2024
概括
双战略材料智能设计框架 (DSMID) 使用机器学习来克服材料科学中的小数据挑战. 它可以有效地发现新的合金,比如高强度,高可塑性优性高透合金 (EHEA).
科学领域:
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
- 计算材料设计设计 计算材料设计
背景情况:
- 材料科学中的小型数据集阻碍了准确的机器学习模型开发.
- 这种限制阻碍了对新材料采用数据驱动的智能设计.
研究的目的:
- 引入双战略材料智能设计框架 (DSMID),以应对小数据和选挑战.
- 用有限的数据提高材料特征和属性预测.
- 简化许多合金候选物的识别和评估.
主要方法:
- 敌对领域 适应式嵌入式生成网络 (AAEG) 用于以最小数据 (90分) 的数据传输和财产预测.
- 自动化材料选和评估管道 (AMSEP) 使用大型语言模型进行有效的候选人识别.
- 在DSMID框架内整合AAEG和AMSEP.
主要成果:
- 成功识别并制备了一种新型的高 entropy 合金 (EHEA),Al14 ((CoCrFe) 19Ni28.28.
- 实现了高性能:1085 MPa的抗拉强度和24%的延伸在造状态下.
- 已证明具有优越的可塑性和强度与现有的优质HEAs (如AlCoCrFeNi2.1.1) 相匹配.
结论:
- DSMID框架有效地解决了材料设计中的小数据限制和广泛选挑战.
- 这种方法降低了成本,提高了发现新材料的效率.
- 为智能材料设计提供了可行的途径,特别是在数据稀缺的情况下.
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