来自中国西北工业大学材料学院的李金山教授和袁睿豪副教授团队，提出了一种创新的材料设计方法，开发了结合无监督聚类、可解释分析和相似性评估的采样框架，其核心是基于已知数据和未知数据的聚类距离进行采样，并以航空发动机高温部件的关键材料——高温合金为例，实现了材料γ'相溶解温度（T_γ'）这一影响高温部件上限使用温度的关键指标的外推预测和高效设计。他们基于开发的采样框架，仅从包含179个样本的稀疏数据集出发，选择并合成了9个具有不同成分的合金，其中三个合金的T_γ'相比于训练数据显著提高了～52 ℃。除此之外，该研究还发现了与高温合金T_γ'呈高度线性关系的关键材料特征：合金原子尺寸差和混合焓。原子尺寸失配的增加会导致形成金属间相，而混合焓表征元素之间的化学相容性和结合力，越低表示所形成的相的稳定性越强。因此，设计具有高的合金原子尺寸差和低的混合焓的高温合金有望得到更高的T_γ'，为材料进一步优化提供了思路和线索。通过与主动学习进行对比分析的基线研究发现，主动学习需要至少四次迭代才能可能达到与本研究一样高T_γ'的合金，并且其成分与原有合金更相识。这进一步验证和本研究提出的采样框架在新合金设计方面的高效性。该研究所提出的采样框架在稀疏数据集上表现优异，所涉及的方法在不同数据中具有通用性，有望扩展到不同材料和性能的优化设计，并激发无监督学习在新材料发现中的应用。

该文近期发表于npj Computational Materials 5,: 94 (2019)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

Figure 1 The key idea of this work with emphasis on the unsupervised learning-based sampling towards extrapolation

Unsupervised learning-aided extrapolation for accelerated design of superalloys 

Weijie Liao, Ruihao Yuan*, Xiangyi Xue, Jun Wang, Jinshan Li*, Turab Lookman

Machine learning has been widely used to guide the search of new materials by learning the patterns underlying available data. However, the pure prediction-oriented search is often biased to interpolation due to the limited data in a large unexplored space. Here we present a sampling framework towards extrapolation, that integrates unsupervised clustering, interpretable analysis and similarity evaluation to sample target candidates with improved properties from a vast search space. Using design of superalloys with improved γ'-phase solvus temperature (Tγ') as a model case, we start with a sparse data and by few experiments we find nine new superalloys with chemistries distinct to those in the training data. Three of them show improved Tγ' by about 50 ℃, a large enhancement for superalloys. Moreover, we find two features characterizing mismatch in atomic size and mixing enthalpy linearly affect Tγ'. This work demonstrates the capability of unsupervised learning to search for new materials when limited data is available.