用于晶体结构逆向设计的约束晶体深度卷积生成对抗网络
Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures
摘要：
自主发现具有所需性质的材料是材料科学的最终目标之一，目前的研究主要集中在基于密度泛函理论计算的高通量筛选和使用机器学习的物理性质的正向建模。应用深度学习技术，我们开发了一种生成模型，可以通过优化潜在空间中的形成能来预测不同的稳定晶体结构。结果表明，物理性质的优化可以集成到生成模型的顶部筛选或反向传播，都有自己的优势。将生成模型应用于Bi-Se二元体系，发现在整个组成范围内都可以得到不同的晶体结构，并且在生成的结构完全弛豫到平衡态后，凸包上的相可以再现。该方法可推广到多组分体系的多目标优化设计，为实现材料性能最优的逆设计奠定了基础。
英文摘要：
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 modelling 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 backwards 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.
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