论文标题
可扩展的高斯工艺，用于预测具有大数据集的无机眼镜的性能
Scalable Gaussian Processes for Predicting the Properties of Inorganic Glasses with Large Datasets
论文作者
论文摘要
高斯工艺回归（GPR）是一种有用的技术，可以预测镜头中的属性关系，因为该方法固有地提供了预测的标准偏差。但是，由于与之相关的大量计算成本，该技术仍然仅限于小型数据集。 Here, using a scalable GPR algorithm, namely, kernel interpolation for scalable structured Gaussian processes (KISS-GP) along with massively scalable GP (MSGP), we develop composition--property models for inorganic glasses based on a large dataset with more than 100,000 glass compositions, 37 components, and nine important properties, namely, density, Young's, shear, and bulk模量，热膨胀系数，Vickers的硬度，折射率，玻璃过渡温度和液体温度。最后，为了加速玻璃设计，这里开发的模型是公开共享的，即玻璃基因组学（Pyggi）的软件包的一部分。
Gaussian process regression (GPR) is a useful technique to predict composition--property relationships in glasses as the method inherently provides the standard deviation of the predictions. However, the technique remains restricted to small datasets due to the substantial computational cost associated with it. Here, using a scalable GPR algorithm, namely, kernel interpolation for scalable structured Gaussian processes (KISS-GP) along with massively scalable GP (MSGP), we develop composition--property models for inorganic glasses based on a large dataset with more than 100,000 glass compositions, 37 components, and nine important properties, namely, density, Young's, shear, and bulk moduli, thermal expansion coefficient, Vickers' hardness, refractive index, glass transition temperature, and liquidus temperature. Finally, to accelerate glass design, the models developed here are shared publicly as part of a package, namely, Python for Glass Genomics (PyGGi).
