Guided manifold learning and semi-supervised visualization with natural out-of-sample extension based on random forest proximities and diffusion geometry-regularized autoencoder architecture.

We identify critical flaws in existing datasets and benchmarking protocols for crystal structure prediction in generative modeling of inorganic crystals; we revise datasets and make new benchmarks that account for crystal polymorphism.