Two-dimensional nanomaterials, such as graphene, have been extensively studied because of their outstanding physical properties. Structure and topology of nanopores on such materials can be important for their performances in real-world engineering applications, like water desalination. However, discovering the most efficient nanopores often involves a very large number of experiments or simulations that are expensive and time-consuming. In this work, we propose a data-driven artificial intelligence (AI) framework for discovering the most efficient graphene nanopore for water desalination. Via a combination of deep reinforcement learning (DRL) and convolutional neural network (CNN), we are able to rapidly create and screen thousands of graphene nanopores and select the most energy-efficient ones. Molecular dynamics (MD) simulations on promising AI-created graphene nanopores show that they have higher water flux while maintaining rival ion rejection rate compared to the normal circular nanopores. Irregular shape with rough edges geometry of AI-created pores is found to be the key factor for their high water desalination performance. Ultimately, this study shows that AI can be a powerful tool for nanomaterial design and screening.

二维纳米材料，如石墨烯，因其优异的物理性能而被广泛研究。此类材料上纳米孔的结构和拓扑对于它们在实际工程应用（如海水淡化）中的性能非常重要。然而，发现最有效的纳米孔通常涉及大量昂贵且耗时的实验或模拟。在这项工作中，我们提出了一个数据驱动的人工智能 (AI) 框架，用于发现用于海水淡化的最有效的石墨烯纳米孔。通过深度强化学习 (DRL) 和卷积神经网络 (CNN) 的结合，我们能够快速创建和筛选数千个石墨烯纳米孔并选择最节能的那些。对有前途的 AI 创造的石墨烯纳米孔的分子动力学 (MD) 模拟表明，与正常的圆形纳米孔相比，它们具有更高的水通量，同时保持竞争对手的离子排斥率。发现 AI 创造的孔隙具有粗糙边缘几何形状的不规则形状是其高海水淡化性能的关键因素。最终，这项研究表明人工智能可以成为纳米材料设计和筛选的强大工具。