  A significant development towards inverse design of materials with
well-defined target properties is reported. A deep generative model based on
variational autoencoder (VAE), conditioned simultaneously by two target
properties, is developed to inverse design stable magnetic materials. Structure
of the physics informed, property embedded latent space of the model is
analyzed using graph theory, based on the idea of similarity index. The graph
idea is shown to be useful for generating new materials that are likely to
satisfy target properties. An impressive ~96% of the generated materials is
found to satisfy the target properties as per predictions from the target
learning branches. This is a huge improvement over approaches that do not
condition the VAE latent space by target properties, or do not consider
connectivity of the parent materials perturbing which the new materials are
generated. In such models, the fraction of materials satisfying targets can be
as low as ~5%. This impressive feat is achieved using a simple real-space only
representation called Invertible Real-space Crystallographic Representation
(IRCR), that can be directly read from material cif files. Model predictions
are finally validated by performing DFT calculations on a randomly chosen
subset of materials. Performance of the present model using IRCR is comparable
or superior to that of the models reported earlier. This model for magnetic
material generation, MagGen, is applied to the problem of designing rare earth
free permanent magnets with promising results.
