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We developed an inverse design framework enabling automated generation of stable multi-component crystal structures by optimizing the formation energies in the latent space based on reversible crystal graphs with continuous representation. It is demonstrated that 9,160 crystal structures can be generated out of 50,000 crystal graphs, leading to 8,310 distinct cases using a training set of 52,615 crystal structures from Materials Project. Detailed analysis on 15 selected systems reveals that unreported crystal structures below the convex hull can be discovered in 6 material systems. Moreover, the generation efficiency can be further improved by considering extra hypothetical structures in the training. This paves the way to perform inverse design of multicomponent materials with possible multi-objective optimization.
Autonomous materials discovery with desired properties is one of the ultimate goals for modern materials science. Applying the deep learning techniques, we have developed a generative model which can predict distinct stable crystal structures by opti
Analysis of the intriguing physical properties of the dodecaborides, $R$B$_{12}$, requires accurate data on their crystal structure. We show that a simple cubic model fits well with the atomic positions in the unit cell but cannot explain the observe
Experimental studies established that calcium undergoes several counterintuitive transitions under pressure: fcc rightarrow bcc rightarrow simple cubic rightarrow Ca-IV rightarrow Ca-V, and becomes a good superconductor in the simple cubic and higher
Shape-morphing finds widespread utility, from the deployment of small stents and large solar sails to actuation and propulsion in soft robotics. Origami structures provide a template for shape-morphing, but rules for designing and folding the structu
The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery. However, thoroughly and efficiently sampling the entire design space in a computationally tractable manner re