  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
