用于晶体结构预测的生成对抗网络
Generative Adversarial Networks for Crystal Structure Prediction
摘要：
对新功能材料的不断需求要求有效的策略来加速材料的设计和发现。在应对这一挑战时，机器学习生成模型可以提供有希望的机会，因为它们允许通过低维潜在空间对化学空间进行连续导航。在这项工作中，我们采用了一种晶体表示，这是无反转与低内存要求的基础上的晶胞信息和分数原子坐标，并建立生成对抗网络（GAN）的晶体结构。将该模型应用于Mg-Mn-O三元无机材料体系，生成具有潜在水裂解光阳极应用前景的新型结构，并结合其光阳极性能评价，用于高通量虚拟筛选（HTVS）。我们建立的生成HTVS系统预测了23种新的晶体结构，具有合理的预测稳定性和带隙。这些研究结果表明，所提出的生成模型可以是一种有效的方式来探索隐藏的部分的化学空间，一个区域，通常是无法到达的，当采用传统的替代为基础的发现。
英文摘要：
The constant demand for new functional materials calls for efficient strategies to accelerate the materials design and discovery. In addressing this challenge, machine learning generative models can offer promising opportunities since they allow for the continuous navigation of chemical space via low dimensional latent spaces. In this work, we employ a crystal representation that is inversion-free with a low memory requirement based on unit cell information and fractional atomic coordinates, and build the generative adversarial network (GAN) for crystal structures. The proposed model is then applied to the Mg-Mn-O ternary inorganic materials system to generate novel structures with application as potential water-splitting photoanodes, and combined with the evaluation of their photoanode properties for high-throughput virtual screening (HTVS). The generative-HTVS system that we built predicts 23 new crystal structures with a reasonable predicted stability and bandgap. These findings suggest that the proposed generative model can be an effective way to explore hidden portions of the chemical space, an area that is usually unreachable when conventional substitution-based discovery is employed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
