Abstract
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.
Highlights
Single-layer graphene, as an iconic two-dimensional (2D) material, has drawn much scientific attention in recent decades
The framework (Fig. 1) of water desalination for efficient water desalination consists of a deep reinforcement learning (DRL) agent and a convolutional neural network (CNN)-based performance predictor network
Given the featurized information of the nanoporous graphene sheet (Morgan fingerprint, Cartesian coordinates of each atom, and geometrical features of graphene membrane from the CNN model) and predicted water flux and ion rejection, the DRL agent was trained to create a pore on graphene sheet with the goal to maximize its performance in the water desalination process
Summary
Single-layer graphene, as an iconic two-dimensional (2D) material, has drawn much scientific attention in recent decades. In RO, the geometry of nanopores in 2D materials plays a determinant role in water desalination performance[9,11]. A large pore that allows high water flux is likely to perform poorly in rejecting ions; a small pore that rejects 100% undesired ions, on the other hand, usually have limited water flux. An optimal nanopore for water desalination is expected to allow as high water flux as possible while maintaining a high ion rejection rate. Finding the optimal nanopore geometry on graphene can be challenging due to high computational and experimental cost associated with extensive experiments, i.e., there are countless possible shapes for a pore on a 4 nm × 4 nm graphene membrane, but evaluating the water flux and ion rejection of a single pore using 10 ns MD simulation takes roughly 36 h on a 56-
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