Abstract 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预测

摘要 非中心对称材料在激光技术、通信系统、量子计算、网络安全等诸多重要应用中发挥着至关重要的作用。然而，新的非中心对称材料的实验发现极其困难。在这里，我们提出了一个机器学习模型，可以预测潜在晶体结构的组成是否是中心对称的。通过评估使用 matminer featurizer 包结合不同机器学习算法计算的一组不同的成分特征，我们发现随机森林分类器为非中心对称材料预测提供了最佳性能，在对上进行 10 倍交叉验证评估时达到了 84.8% 的准确率从 Materials Project 中提取的包含 82,506 个样本的数据集。使用只有 3 个元素的材料训练的随机森林模型的准确度甚至更高，达到 86.9%。我们应用我们的 ML 模型从我们的逆向设计引擎生成的 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 到 4 个元素的前 20 种候选非中心对称材料和前 20 种硼酸盐候选材料。