利用有序网络和机器学习确定液晶性质
Determining liquid crystal properties with ordinal networks and machine learning
摘要：
机器学习方法对于材料科学的发展正变得越来越重要。尽管如此，在这些系统的开发中使用图像分析仍然是最近的和未充分探索的，特别是在经常通过光学成像技术研究的材料中，例如液晶。在这里，我们应用最近提出的有序网络的方法来映射光学纹理从实验样品的液晶到复杂的网络，并使用这种表示与一个简单的统计学习算法，以调查这些材料的不同的物理性质。我们的研究表明，有序网络形成的只有24个节点编码的关键信息的液晶性能，从而使我们能够训练简单机械学习模型，能够识别和分类中间相转变，区分不同的掺杂浓度用于诱导手性中间相，并预测样品温度具有出色的精度。我们的方法的精度和可扩展性表明，它可以用于探测涉及大规模数据集或实时监控系统的情况下，不同材料的属性。
英文摘要：
Machine learning methods are becoming increasingly important for the development of materials science. In spite of this, the use of image analysis in the development of these systems is still recent and underexplored, especially in materials often studied via optical imaging techniques such as liquid crystals. Here we apply the recently proposed method of ordinal networks to map optical textures obtained from experimental samples of liquid crystals into complex networks and use this representation jointly with a simple statistical learning algorithm to investigate different physical properties of these materials. Our research demonstrates that ordinal networks formed by only 24 nodes encode crucial information about liquid crystal properties, thus allowing us to train simple machine learning models capable of identifying and classifying mesophase transitions, distinguishing among different doping concentrations used to induce chiral mesophases, and predicting sample temperatures with outstanding accuracy. The precision and scalability of our approach indicate it can be used to probe properties of different materials in situations involving large-scale datasets or real-time monitoring systems.
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