Autonomous materials discovery with desired properties is one of the ultimate goals for materials science,and the current studies have been focusing mostly on high-throughput screening based on density functional theo...Autonomous materials discovery with desired properties is one of the ultimate goals for materials science,and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modeling of physical properties using machine learning.Applying the deep learning techniques,we have developed a generative model,which can predict distinct stable crystal structures by optimizing the formation energy in the latent space.It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backward propagator,both with their own advantages.Applying the generative models on the binary Bi-Se system reveals that distinct crystal structures can be obtained covering the whole composition range,and the phases on the convex hull can be reproduced after the generated structures are fully relaxed to the equilibrium.The method can be extended to multicomponent systems for multi-objective optimization,which paves the way to achieve the inverse design of materials with optimal properties.展开更多
基金The authors gratefully acknowledge computational time on the Lichtenberg High-Performance Supercomputer.Teng Long thanks the financial support from the China Scholarship Council(CSC).Part of this work was supported by the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(Grant No.743116-project Cool Innov)This work was also supported by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)–Project-ID 405553726–TRR 270We also acknowledge support by the Deutsche Forschungsgemeinschaft(DFG–German Research Foundation)and the Open Access Publishing Fund of Technical University of Darmstadt.
文摘Autonomous materials discovery with desired properties is one of the ultimate goals for materials science,and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modeling of physical properties using machine learning.Applying the deep learning techniques,we have developed a generative model,which can predict distinct stable crystal structures by optimizing the formation energy in the latent space.It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backward propagator,both with their own advantages.Applying the generative models on the binary Bi-Se system reveals that distinct crystal structures can be obtained covering the whole composition range,and the phases on the convex hull can be reproduced after the generated structures are fully relaxed to the equilibrium.The method can be extended to multicomponent systems for multi-objective optimization,which paves the way to achieve the inverse design of materials with optimal properties.
