Predicting the onset temperature (Tg) of Ge Se1− glass transition: a feature selection based two-stage support vector regression method

High-throughput computational screening of oxide double perovskites for optoelectronic and photocatalysis applications

“…Moreover, this method can be used in other fields, such as semiconductor light-emitting diodes and transparent conducting materials [31]. In addition, novel materials design via machine learning has drawn much interest in many fields, such as photovoltaic materials [32], lithium-ion battery materials [33,34], and glass material system [35,36]. Moreover, this approach is being optimized and updated continually [37,38].…”

Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry

“…(1) Macro and micro-performance predictions, such as glass transition temperature [36,37], metal corrosion behavior, lattice constant and lithium-ion diffusion mechanism. (2) Property of predicted materials, such as material structure and performance.…”

Machine Learning‐Aided Process Design: Modeling and Prediction of Transformation Temperature for Pearlitic Steel

“…[15] In recent years, machine learning (ML) approaches are rapidly revolutionizing many fields with an extraordinary growing research interest. [16][17][18][19][20][21][22][23] Given that, Shi et al [24][25][26] have reviewed the application of ML method in materials engineering as well as the functional field. In addition, we identify several common themes associated with the application of ML approaches, such as predictions of phase diagrams, [27] crystal structures, [28] damage identification, [29][30][31][32][33] and materials properties, [34][35][36][37] which significantly accelerates the discovery of new materials via a data-driven materials research approach.…”