Scalable Gaussian Processes for Predicting the Properties of Inorganic Glasses with Large Datasets

Predicting second virial coefficients of organic and inorganic compounds using Gaussian Process Regression

We show that by using intuitive and accessible molecular features it is possible to predict the temperature-dependent second virial coefficient of organic and inorganic compounds using Gaussian process regression. In particular, we built a low dimensional representation of features based on intrinsic molecular properties, topology and physical properties relevant for the characterization of molecule-molecule interactions. The featurization was used to predict second virial coefficients in the interpolative regime with a relative error $\lesssim 1\% $ and to extrapolate the prediction to temper…

Predicting Young's Modulus of Glasses with Sparse Datasets using Machine Learning

Machine learning (ML) methods are becoming popular tools for the prediction and design of novel materials. In particular, neural network (NN) is a promising ML method, which can be used to identify hidden trends in the data. However, these methods rely on large datasets and often exhibit overfitting when used with sparse dataset. Further, assessing the uncertainty in predictions for a new dataset or an extrapolation of the present dataset is challenging. Herein, using Gaussian process regression (GPR), we predict Young's modulus for silicate glasses having sparse dataset. We show that GPR sign…

Predicting densities and elastic moduli of SiO2-based glasses by machine learning

Chemical design of SiO2-based glasses with high elastic moduli and low weight is of great interest. However, it is difficult to find a universal expression to predict the elastic moduli according to the glass composition before synthesis since the elastic moduli are a complex function of interatomic bonds and their ordering at different length scales. Here we show that the densities and elastic moduli of SiO2-based glasses can be efficiently predicted by machine learning (ML) techniques across a complex compositional space with multiple (>10) types of additive oxides besides SiO2. Our machine …

Predicting the propensity for thermally activated $β$ events in metallic glasses via interpretable machine learning

The elementary excitations in metallic glasses (MGs), i.e., $β$ processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated $β$ processes (i.e., shear transformations) from the static structure. However, the complex tens…

通过机器学习从组成薄膜的光学性质预测有机-无机卤化物钙钛矿光伏性能

Predicting Organic-Inorganic Halide Perovskite Photovoltaic Performance from Optical Properties of Constituent Films through Machine Learning

本研究提出一种机器学习方法，以钙钛矿薄膜的三项光学测量结果为输入，可预测对应太阳能电池的光伏参数并识别退化电池，是首个仅靠材料光学性质预测器件光伏性能的机器学习研究。

Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases

Metallic glasses have attracted considerable interest in recent years due to their unique combination of superb properties and processability. Predicting bulk metallic glass formers from known parameters remains a challenge and the search for new systems is still performed by trial and error. It has been speculated that some sort of "confusion" during crystallization of the crystalline phases competing with glass formation could play a key role. Here, we propose a heuristic descriptor quantifying confusion and demonstrate its validity by detailed experiments on two well-known glass forming all…