A machine learning based approach for phononic crystal property discovery
A machine learning based approach for phononic crystal property discovery
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DOI：
10.1063/5.0006153
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发表时间：
2020-07
影响因子：
3.2
通讯作者：
Seid M. Sadat;Robert Y. Wang
中科院分区：
文献类型：
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作者：
Seid M. Sadat;Robert Y. Wang
声子晶体是一种人工结构的材料，它具有特殊的振动特性，可以对声音和热传输进行高级操作。这些特殊的性质源于一个带隙的形成，该带隙阻止了声子带图中整个频率范围的激发。不幸的是，识别具有有效带隙的声子晶体是一个有问题的过程，因为不是所有的声子晶体都有带隙。预测声子晶体结构是否有带隙，如果有，则带隙的中心频率和宽度是一个计算昂贵的过程。在这里，我们探索机器学习作为快速发现声子带隙存在，中心频率和宽度的快速筛选工具。我们测试了三种不同的机器学习算法（逻辑/线性回归、人工神经网络和随机森林），并表明随机森林的表现最好。例如，我们表明随机声子晶体选择具有带隙的概率只有17%，而在与随机森林模型结合快速筛选后，这一概率增加到89%。在预测带隙中心频率和宽度时，该模型的确定系数分别为0.66和0.85。如果模型先验地知道存在带隙，则中心和宽度的决定系数分别提高到0.97和0.85。我们表明，大多数模型的性能增益是在小到~ 5000个样本的训练数据集上实现的。仅用500个样本训练模型会导致性能下降，但仍然产生具有预测值的算法。
Phononic crystals are artificially structured materials that can possess special vibrational properties that enable advanced manipulations of sound and heat transport. These special properties originate from the formation of a bandgap that prevents the excitation of entire frequency ranges in the phononic band diagram. Unfortunately, identifying phononic crystals with useful bandgaps is a problematic process because not all phononic crystals have bandgaps. Predicting if a phononic crystal structure has a bandgap, and if so, the gap's center frequency and width is a computationally expensive process. Herein, we explore machine learning as a rapid screening tool for expedited discovery of phononic bandgap presence, center frequency, and width. We test three different machine learning algorithms (logistic/linear regression, artificial neural network, and random forests) and show that random forests performs the best. For example, we show that a random phononic crystal selection has only a 17% probability of having a bandgap, whereas after incorporating rapid screening with the random forests model, this probability increases to 89%. When predicting the bandgap center frequency and width, this model achieves coefficient of determinations of 0.66 and 0.85, respectively. If the model has a priori knowledge that a bandgap exists, the coefficients of determination for center and width improve to 0.97 and 0.85, respectively. We show that most of the model's performance gains are achieved for training datasets as small as ∼5000 samples. Training the model with just 500 samples led to reduced performance but still yielded algorithms with predictive values.