Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model
Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model
复制标题
使用生成对抗网络模型快速发现高硬度多主元素合金
DOI：
10.1016/j.actamat.2023.119177
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发表时间：
2023
期刊：
影响因子：
9.4
通讯作者：
Johnson, Duane D.
中科院分区：
文献类型：
--
作者：
Roy, Ankit;Hussain, Aqmar;Sharma, Prince;Balasubramanian, Ganesh;Taufique, M.F.N.;Devanathan, Ram;Singh, Prashant;Johnson, Duane D.
多主元素合金（MPEAs）由于其具有良好的高温组织和力学性能而不断获得研究的重视。最近，机器学习（ML）和材料信息学已被广泛用于筛选MPEAs，然而，这些努力大多集中在构建分类和回归模型，用于预测已知组合物的相稳定性和机械性能。这些方法可以加速筛选过程，但在实际时间范围内从无限大的MPEA系统设计空间优化具有所需性质的新组合物仍然是一个巨大的挑战。为了解决这一成分优化挑战，利用与神经网络ML模型相结合的生成对抗网络通过过滤具有高硬度的成分来设计MPEAs。即使在具有18个元素作为描述符的高维空间中，ML模型也能够生成优化的组合物，其中一种组合物的硬度（941 HV）比训练数据中的最大值（857 HV）高10%。密度泛函理论被用来提供热力学和电子学的见解，以更高的硬度的新MPEA发现。目前的工作可以优化的成分从广泛的设计空间的18个元素（包括W，Ta和Nb），提出了一个机会，合成新的组合物的应用范围从耐腐蚀合金到核材料。研究结果表明，生成ML可以通过识别新的成分来大大加速材料发现，这可以作为指导实验的数据信息工具。
Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, agenerative adversarial networkcoupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. The findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.