Designing mechanically tough graphene oxide materials using deep reinforcement learning
Designing mechanically tough graphene oxide materials using deep reinforcement learning
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DOI：
10.1038/s41524-022-00919-z
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发表时间：
2022-11
影响因子：
9.7
通讯作者：
Bowen Zheng;Zeyu Zheng;Grace X. Gu
中科院分区：
文献类型：
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作者：
Bowen Zheng;Zeyu Zheng;Grace X. Gu
氧化石墨烯（GO）在许多技术中发挥着越来越重要的作用。然而，如何战略性地分配功能组以进一步提高性能仍然没有答案。我们利用深度强化学习（RL）来设计机械坚韧的机器人。设计任务被制定为一个顺序的决策过程，并采用政策梯度RL模型，以最大限度地提高GO的韧性。结果表明，我们的方法可以稳定地产生的韧性值超过两个标准偏差以上的随机分布的平均值的官能团分布。此外，我们的RL方法仅在5000次展示中就达到了优化的官能团分布，而最简单的设计任务有2 × 1011种可能性。最后，我们表明，我们的方法是可扩展的官能团密度和GO大小。本研究展示了官能团分布对GO性质的影响，并说明了深度RL方法的有效性和数据效率。
Graphene oxide (GO) is playing an increasing role in many technologies. However, it remains unanswered how to strategically distribute the functional groups to further enhance performance. We utilize deep reinforcement learning (RL) to design mechanically tough GOs. The design task is formulated as a sequential decision process, and policy-gradient RL models are employed to maximize the toughness of GO. Results show that our approach can stably generate functional group distributions with a toughness value over two standard deviations above the mean of random GOs. In addition, our RL approach reaches optimized functional group distributions within only 5000 rollouts, while the simplest design task has 2 × 1011possibilities. Finally, we show that our approach is scalable in terms of the functional group density and the GO size. The present research showcases the impact of functional group distribution on GO properties, and illustrates the effectiveness and data efficiency of the deep RL approach.