A very active area of materials research is to devise methods that use
machine learning to automatically extract predictive models from existing
materials data. While prior examples have demonstrated successful models for
some applications, many more applications exist where machine learning can make
a strong impact. To enable faster development of machine-learning-based models
for such applications, we have created a framework capable of being applied to
a broad range of materials data. Our method works by using a chemically diverse
list of attributes, which we demonstrate are suitable for describing a wide
variety of properties, and a novel method for partitioning the data set into
groups of similar materials in order to boost the predictive accuracy. In this
manuscript, we demonstrate how this new method can be used to predict diverse
properties of crystalline and amorphous materials, such as band gap energy and
glass-forming ability.

该研究论文介绍了一种新的机器学习框架，用于从现有的材料数据中提取预测模型，方法是使用具有化学多样性的属性列表，并将数据集分成相似材料组，提高预测准确度，可用于预测晶态和非晶材料的多种属性。