Unified Model for Crystalline Material Generation

One of the greatest challenges facing our society is the discovery of new innovative crystal materials with specific properties. Recently, the problem of generating crystal materials has received increasing attention, however, it remains unclear to what extent, or in what way, we can develop generative models that consider both the periodicity and equivalence geometric of crystal structures. To alleviate this issue, we propose two unified models that act at the same time on crystal lattice and atomic positions using periodic equivariant architectures. Our models are capable to learn any arbitrary crystal lattice deformation by lowering the total energy to reach thermodynamic stability. Code and data are available at https://github.com/aklipf/GemsNet.

Mind2Mind : transfer learning for GANs

We propose an approach for transfer learning with GAN architectures. In general, transfer learning enables deep networks for classification tasks to be trained with limited computing and data resources. However a similar approach is missing in the specific context of generative tasks. This is partly due to the fact that the extremal layers of the two networks of a GAN, which should be learned during transfer, are on two opposite sides. This requires back-propagating information through both networks, which is computationally expensive. We develop a method to directly train these extremal layers against each other, by-passing all the intermediate layers. We also prove rigorously, for Wasserstein GANs, a theorem ensuring the convergence of the learning of the transferred GAN. Finally, we compare our method to state-of-the-art methods and show that our method converges much faster and requires less data.