论文标题
物理学指导深度学习，用于具有对称约束的水晶材料的生成设计
Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints
论文作者
论文摘要
发现新材料是材料科学对人类社会进步至关重要的挑战。基于实验和模拟的传统方法是劳动密集型或昂贵的，取决于专家的启发式知识，成功的方法很大。在这里，我们提出了一种基于深度学习的物理学指导的晶体生成模型（PGCGM），用于具有高结构多样性和对称性的有效晶体材料设计。与我们以前的Cupicgan模型相比，我们的模型将生成有效性提高了700 \％，这是最新的结构发电机之一，是最新的结构发电机之一，并且增加了45 \％。密度功能理论（DFT）计算用于验证具有1,869个材料的生成的结构成功优化并沉积到Carolina材料数据库\ url {www.carolinamatdb.org}中和潜在的合成性。
Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts' heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design with high structural diversity and symmetry. Our model increases the generation validity by more than 700\% compared to FTCP, one of the latest structure generators and by more than 45\% compared to our previous CubicGAN model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1,869 materials out of 2,000 are successfully optimized and deposited into the Carolina Materials Database \url{www.carolinamatdb.org}, of which 39.6\% have negative formation energy and 5.3\% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potential synthesizability.
