It then presents an assortment of examples of recent machine learning applications within materials science. The chapter also discusses a range of emerging efforts, including high-throughput phase diagram and crystal structure determination methods, accelerated prediction of materials properties, development ...
Crystal structure prediction is a long-standing challenge in condensed matter and chemical science. Here we report a machine-learning approach for crystal structure prediction, in which a graph network (GN) is employed to establish a cor... G Cheng,XG Gong,WJ Yin - 《Nature Communications》 被...
Recently, machine learning has been shown to accelerate the discovery of new materials for dielectric polymers5, OLED displays6, and polymeric dispersants7. In the realm of molecules, ML has been applied successfully to the prediction of atomization energies8, bond energies9, dielectric breakdown str...
Then, the Ef predicted by the model was used as the instrumental variable to build a progressive learning model to predict the Eg of the perovskite materials. The results of the model indicated that the addition of predicted Ef as an instrumental descriptor can promote the prediction accuracy of...
Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions 来自 ACS 喜欢 0 阅读量: 91 作者:Y Zhao,Y Cui,Z Xiong,J Jin,J Hu 摘要: Structural information of materials such as the crystal systems and space groups are highly useful for analyzing ...
Predicting reaction performance in C–N cross-coupling using machine learningscience.sciencemag.org/content/early/2018/02/14/science.aar5169 作者自己是这么写的 机器学习方法正在成为众多学科科学研究的组成部分。 在这里,我们证明机器学习可以用来预测在多维化学空间中使用通过高通量实验获得的数据的合成反应的...
Predicting reaction performance in C–N cross-coupling using machine learningscience.sciencemag.org/content/early/2018/02/14/science.aar5169 作者自己是这么写的 机器学习方法正在成为众多学科科学研究的组成部分。 在这里,我们证明机器学习可以用来预测在多维化学空间中使用通过高通量实验获得的数据的合成反应的...
crystal structure prediction; machine learning; K-nearest neighbours; lithium-ion battery; cathodes; iron; manganese1. Introduction Lithium-ion batteries have, without any doubt, contributed significantly to the development of portable electronics that has occurred over the past thirty years. They are ...
leaves - A pure Go implementation of the prediction part of GBRTs, including XGBoost and LightGBM. gobrain - Neural Networks written in Go. go-featureprocessing - Fast and convenient feature processing for low latency machine learning in Go. go-mxnet-predictor - Go binding for MXNet c_predict...
How to represent crystal structures for machine learning: towards fast prediction of electronic properties Phys Rev B, 89 (2014), p. 205118 CrossrefView in ScopusGoogle Scholar [111] W.W. Ju Research on materials properties prediction based on machine learning method Master Degree Thesis Shanghai...