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Brief: Researchers have developed a novel architecture-agnostic method for the generation of atomic structures for use in materials discovery.

Accomplishment: A primary prerequisite for developing generative models for atomic structures is an invertible input representation. Currently, there are no suitable invertible representations for crystal structures like the Simplified molecular-input line-entry system (SMILES) representation used in molecular generation, which significantly hinders progress in this area.  Here, researchers developed a computational approach to reconstruct non-invertible representations back into the Cartesian atomic coordinates which define a material exactly.  This is done by using a gradient-based global optimization algorithm to solve for the optimal atomic coordinates, using gradients obtained from automatic differentiation.  This can then be coupled to a generative machine learning model which generates new materials within the representation space, rather than the data-inefficient Cartesian space.  This two-step process is model-agnostic and does not require a specialized machine learning architecture for the generation of atomic structures, which allows one to harness effective existing generative algorithms such as invertible neural networks.  This approach thereby provides a promising solution for the problem of materials design at the atomic level.