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We use the machine learning technique to search the polytope which can resultin an orientifold Calabi-Yau hypersurface and the "naive Type IIB stringvacua". We show that neural networks can be trained to give a high accuracy forclassifying the orientifold property and vacua based on the newly generatedorientifold Calabi-Yau database with $h^{1,1}(X) \leq 6$ arXiv:2111.03078. Thisindicates the orientifold symmetry may already be encoded in the polytopestructure. In the end, we try to use the trained neural networks model to gobeyond the database and predict the orientifold signal of polytope for higher$h^{1,1}(X)$.

Applying machine learning to the Calabi-Yau orientifolds with string vacua