Predicting molecular ordering in a binary liquid crystal using machine learning
Predicting molecular ordering in a binary liquid crystal using machine learning
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DOI：
10.1080/02678292.2019.1656293
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发表时间：
2019-08-26
期刊：
影响因子：
2.2
通讯作者：
Arai, Noriyoshi
中科院分区：
文献类型：
--
作者：
Inokuchi, Takuya;Okamoto, Ryosuke;Arai, Noriyoshi
机器学习是人工智能技术的一种，在许多行业的产品开发周期中都有应用。据报道，机器学习可以应用于预测功能材料的化学性质，例如，表面活性剂溶液的自组装（Nanoscale, 2018,10,16013）和铜纳米颗粒的化学反应(ACS Energy Lett。， 2018, 3, 2983)。液晶分子是一种具有代表性的功能材料。单分散体系已成为许多理论研究的目标；然而，最终产物通常具有多分散分布。在一些研究中已经报道了多分散体系所表现出的性质与单分散体系所观察到的性质不同。在这项研究中，我们关注多分散体系的物理性质，即二元LC分子的比例对相变温度的影响，以及使用机器学习预测系统物理性质的可能性。利用机器学习获得了较高的预测精度。该研究表明，机器学习可以用于预测多分散LC体系的相变温度。本研究结果将通过机器学习的应用，为材料科学和分子设计的进一步发展做出贡献。
Machine learning is a category of AI technology that is utilized in the product development cycle in numerous industries. It was reported that machine learning can be applied to predict the chemical properties of functional materials, for example, the self-assembly of surfactant solutions (Nanoscale, 2018, 10, 16013) and the chemical reactions of copper nanoparticles (ACS Energy Lett., 2018, 3, 2983). A liquid crystal (LC) molecule is a representative functional material. Monodisperse systems have been targeted in many theoretical studies; however, the resulting products usually have a polydisperse distribution. It has been reported in several studies that the properties exhibited by polydisperse systems are different from those observed in monodisperse systems. In this study, we focus on the physical properties of polydisperse systems i.e. the ratio of binary LC molecules on the phase transition temperature and the possibility of predicting the physical properties of the system using machine learning. A relatively high prediction accuracy was obtained using machine learning. This study demonstrates that machine learning can be utilized to predict the phase transition temperature of polydispersed LC systems. The results of this study will contribute to the further development of material science and molecular design via the application of machine learning.