Kim Baekjun, Lee Sangwon, Kim Jihan
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Sci Adv. 2020 Jan 3;6(1):eaax9324. doi: 10.1126/sciadv.aax9324. eCollection 2020 Jan.
Generating optimal nanomaterials using artificial neural networks can potentially lead to a notable revolution in future materials design. Although progress has been made in creating small and simple molecules, complex materials such as crystalline porous materials have yet to be generated using any of the neural networks. Here, we have implemented a generative adversarial network that uses a training set of 31,713 known zeolites to produce 121 crystalline porous materials. Our neural network takes in inputs in the form of energy and material dimensions, and we show that zeolites with a user-desired range of 4 kJ/mol methane heat of adsorption can be reliably produced using our neural network. The fine-tuning of user-desired capability can potentially accelerate materials development as it demonstrates a successful case of inverse design of porous materials.
使用人工神经网络生成最优纳米材料可能会在未来的材料设计中引发显著变革。尽管在创建小分子和简单分子方面已取得进展，但诸如结晶多孔材料等复杂材料尚未通过任何神经网络生成。在此，我们实现了一个生成对抗网络，该网络使用31713种已知沸石的训练集来生成121种结晶多孔材料。我们的神经网络以能量和材料尺寸的形式接收输入，并且我们表明，使用我们的神经网络能够可靠地生产出甲烷吸附热在用户期望的4 kJ/mol范围内的沸石。用户期望能力的微调有可能加速材料开发，因为它展示了多孔材料逆向设计的成功案例。
