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Received:December 07, 2020 Revised:January 08, 2021
Received:December 07, 2020 Revised:January 08, 2021
中文摘要: 在MOFs研究领域, 探寻新型MOFs仍然是非常困难的研究问题. 将MOFs进行“材料基因编码”后, 应用遗传算法(Genetic Algorithm, GA)可以快速探索新型MOFs, 但其性能依赖于设定的个体适应度函数, 且对新生成的MOFs个体的有效评估也影响了该方法的效果. 机器学习方法可以对MOFs的构效关系进行评估与预测, 人工神经网络(Artificial Neural Network, ANN)是众多机器学习方法中具有代表性的一个, 可以发掘非线性的构效关系. 本文提出将神经网络用于预测遗传算法生成的新型MOFs个体对CH4气体的吸附能力, 从而帮助遗传算法搜索新型MOFs. 实验结果表明, 神经网络可以有效评估新型MOFs材料, 证明了将神经网络与遗传算法相结合用于新型MOFs搜索和筛选的可行性.
Abstract:In the field of MOFs research, searching for novel MOFs is still a complicated problem. After MOFs are processed by “material genetic encoding”, the Genetic Algorithm (GA) can be used to rapidly explore novel MOFs, but their performance depends on the setting of individual fitness functions, and the effective evaluation of the novel MOFs also contributes to the effectiveness of this method. As one of the representative methods of machine learning, the Artificial Neural Network (ANN) can uncover the non-linear constitutive relationships. In this paper, the neural network is introduced to predict the adsorption capacity for CH4 gas by the novel MOFs generated by GA, thereby facilitating the search for novel MOFs by GA. The experimental results show that the neural network can thoroughly evaluate the novel MOFs materials, demonstrating the feasibility of combining the neural network and GA for the search and screening of the novel MOFs.
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基金项目:国家自然科学基金(22078004); 中央高校基础研究基金(buctrc201727); 北京化工大学大科学项目(XK180301)
引用文本：
赖欣,卢罡,王磊,毕志远,阳庆元,俞度立.基于ANN的新型MOFs性能预测.计算机系统应用,2021,30(9):1-11
LAI Xin,LU Gang,WANG Lei,BI Zhi-Yuan,YANG Qing-Yuan,YU Du-Li.ANN-Based Prediction about Performance of Novel MOFs.COMPUTER SYSTEMS APPLICATIONS,2021,30(9):1-11
赖欣,卢罡,王磊,毕志远,阳庆元,俞度立.基于ANN的新型MOFs性能预测.计算机系统应用,2021,30(9):1-11
LAI Xin,LU Gang,WANG Lei,BI Zhi-Yuan,YANG Qing-Yuan,YU Du-Li.ANN-Based Prediction about Performance of Novel MOFs.COMPUTER SYSTEMS APPLICATIONS,2021,30(9):1-11
