The problem of feature extraction, in crystalline solid‐state systems with point defects, is considered. Novel methods for creating features for use in machine‐learning‐based predictive modeling of such systems are developed. The methods are illustrated in a case study where machine learning methods are used to predict the onset of amorphization in crystalline systems containing vacancy defects. How the methods developed may be generalized to study other problems in solid‐state materials is also discussed.

中文翻译：

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用于机器学习的固态晶格特征工程

考虑了在具有点缺陷的晶体固态系统中进行特征提取的问题。开发了用于创建特征以用于此类系统的基于机器学习的预测建模的新颖方法。在案例研究中说明了这些方法，其中使用了机器学习方法来预测包含空位缺陷的晶体系统中非晶化的开始。还讨论了如何推广开发的方法以研究固态材料中的其他问题。