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| 1 | 机器学习驱动难熔高熵合金设计的现状与展望显示文摘难熔高熵合金兼具高强度、高硬度、抗高温氧化等优异综合性能,在航空、航天、核能等领域具有广阔的应用前景和研究价值。但难熔高熵合金成分复杂、设计难度高,严重制约了高性能难熔高熵合金的进一步发展。近年来,机器学习凭借着高效准确的建模预测能力,逐步应用于高性能合金的设计和开发。本文在广泛收集机器学习驱动难熔高熵合金设计研究成果的基础上,详细综述了机器学习在辅助合金相结构设计、力学性能预测、强化机理分析和加速原子模拟等方面的应用与进展。最后,总结了该领域当前存在的不足,并针对如何推进高性能难熔高熵合金的设计进行了展望,包括构建难熔高熵合金高质量数据集、建立难熔高熵合金“成分-工艺-组织-性能”定量关系、实现高性能难熔高熵合金的多目标优化等。 | 高田创 高建宝 李谦 张利军 | 2024 | 材料工程2024，52，1： | 0 |
| 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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