Song Zhilong, Fan Linfeng, Lu Shuaihua, Ling Chongyi, Zhou Qionghua, Wang Jinlan
Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing, 21189, China.
Suzhou Laboratory, Suzhou, 215125, China.
Nat Commun. 2025 Jan 26;16(1):1053. doi: 10.1038/s41467-024-55613-z.
Directly generating material structures with optimal properties is a long-standing goal in material design. Traditional generative models often struggle to efficiently explore the global chemical space, limiting their utility to localized space. Here, we present a framework named Material Generation with Efficient Global Chemical Space Search (MAGECS) that addresses this challenge by integrating the bird swarm algorithm and supervised graph neural networks, enabling effective navigation of generative models in the immense chemical space towards materials with target properties. Applied to the design of alloy electrocatalysts for CO reduction (CORR), MAGECS generates over 250,000 structures, achieving a 2.5-fold increase in high-activity structures (35%) compared to random generation. Five predicted alloys- CuAl, AlPd, SnPd, SnPd, and CuAlSe are synthesized and characterized, with two showing around 90% Faraday efficiency for CORR. This work highlights the potential of MAGECS to revolutionize functional material development, paving the way for fully automated, artificial intelligence-driven material design.
直接生成具有最优性能的材料结构是材料设计中长期以来的目标。传统生成模型常常难以高效探索全局化学空间，限制了它们在局部空间的效用。在此，我们提出了一个名为“基于高效全局化学空间搜索的材料生成”（MAGECS）的框架，该框架通过整合鸟群算法和监督图神经网络来应对这一挑战，使生成模型能够在巨大的化学空间中有效导航，以找到具有目标性能的材料。应用于用于CO还原（CORR）的合金电催化剂设计时，MAGECS生成了超过250,000种结构，与随机生成相比，高活性结构（35%）增加了2.5倍。合成并表征了五种预测合金——CuAl、AlPd、SnPd、SnPd和CuAlSe，其中两种在CORR中显示出约90%的法拉第效率。这项工作突出了MAGECS在变革功能材料开发方面的潜力，为全自动化、人工智能驱动的材料设计铺平了道路。
