Noncentrosymmetric materials play a critical role in many important applications such as laser technology, communication systems,quantum computing, cybersecurity, and etc. However, the experimental discovery of new noncentrosymmetric materials is extremely difficult. Here we present a machine learning model that could predict whether the composition of a potential crystalline structure would be centrosymmetric or not. By evaluating a diverse set of composition features calculated using matminer featurizer package coupled with different machine learning algorithms, we find that Random Forest Classifiers give the best performance for noncentrosymmetric material prediction, reaching an accuracy of 84.8% when evaluated with 10 fold cross-validation on the dataset with 82,506 samples extracted from Materials Project. A random forest model trained with materials with only 3 elements gives even higher accuracy of 86.9%. We apply our ML model to screen potential noncentrosymmetric materials from 2,000,000 hypothetical materials generated by our inverse design engine and report the top 20 candidate noncentrosymmetric materials with 2 to 4 elements and top 20 borate candidates

中文翻译：

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基于机器学习的非中心对称晶体材料的预测

非中心对称材料在许多重要应用中起着关键作用，例如激光技术，通信系统，量子计算，网络安全等。但是，新的非中心对称材料的实验发现非常困难。在这里，我们提出了一种机器学习模型，该模型可以预测潜在晶体结构的组成是否为中心对称。通过评估使用matminer featurizer程序包结合不同机器学习算法计算出的各种成分特征，我们发现随机森林分类器可为非中心对称材料预测提供最佳性能，当在10倍交叉验证中进行评估时，可达到84.8％的准确度该数据集包含从Material Project中提取的82,506个样本。仅使用3种元素的材料训练的随机森林模型可提供86.9％的更高准确性。我们应用ML模型从逆设计引擎生成的2,000,000种假设材料中筛选潜在的非中心对称材料，并报告具有2-4个元素的前20个候选非中心对称材料和20个硼酸盐候选者