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A significant development towards inverse design of materials withwell-defined target properties is reported. A deep generative model based onvariational autoencoder (VAE), conditioned simultaneously by two targetproperties, is developed to inverse design stable magnetic materials. Structureof the physics informed, property embedded latent space of the model isanalyzed using graph theory, based on the idea of similarity index. The graphidea is shown to be useful for generating new materials that are likely tosatisfy target properties. An impressive ~96% of the generated materials isfound to satisfy the target properties as per predictions from the targetlearning branches. This is a huge improvement over approaches that do notcondition the VAE latent space by target properties, or do not considerconnectivity of the parent materials perturbing which the new materials aregenerated. In such models, the fraction of materials satisfying targets can beas low as ~5%. This impressive feat is achieved using a simple real-space onlyrepresentation called Invertible Real-space Crystallographic Representation(IRCR), that can be directly read from material cif files. Model predictionsare finally validated by performing DFT calculations on a randomly chosensubset of materials. Performance of the present model using IRCR is comparableor superior to that of the models reported earlier. This model for magneticmaterial generation, MagGen, is applied to the problem of designing rare earthfree permanent magnets with promising results.

MagGen: A graph aided deep generative model for inverse design of stable, permanent magnets