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| 1 | 数据驱动的航空发动机材料设计研究进展显示文摘数据驱动的材料设计模式已经广泛应用于材料科学研究中,以加速新材料从研发到应用的进程。机器学习技术能够挖掘数据中存在的潜在模式或规律,是数据驱动材料设计模式的研究关键和热点。其中,基于主动学习的策略已经成功指导了多种新材料的开发。首先,介绍了主动学习的研究思路、构成要素及每个环节常用的方法;其次,以航空发动机材料如高温合金、钛基合金、复合材料、热障涂层等为例,介绍了机器学习在其中的具体应用和取得的效果;最后,针对航空发动机材料的恶劣服役环境,展望了机器学习应用于航空发动机材料面临的挑战,并提出了可能的解决方案。 | 袁睿豪 廖玮杰 唐斌 樊江昆 王军 寇宏超 李金山 | 2021 | 航空制造技术2021，64，18： | 2 |
| 2 | A neural network model for high entropy alloy design显示文摘A neural network model is developed to search vast compositional space of high entropy alloys(HEAs).The model predicts the mechanical properties of HEAs better than several other models.It’s because the special structure of the model helps the model understand the characteristics of constituent elements of HEAs.In addition,thermodynamics descriptors were utilized as input to the model so that the model predicts better by understanding the thermodynamic properties of HEAs.A conditional random search,which is good at finding local optimal values,was selected as the inverse predictor and designed two HEAs using the model.We experimentally verified that the HEAs have the best combination of strength and ductility and this proves the validity of the model and alloy design method.The strengthening mechanism of the designed HEAs is further discussed based on microstructure and lattice distortion effect.The present alloy design approach,specialized in finding multiple local optima,could help researchers design an infinite number of new alloys with interesting properties. | Jaemin Wang Hyeonseok Kwon Hyoung Seop Kim Byeong-Joo Lee | 2023 | npj Computational Materials2023，，1： | 0 |
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