Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …

Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …

Abstract Machine learning models are increasingly used in materials studies because of
their exceptional accuracy. However, the most accurate machine learning models are …

Traditional methods of discovering new materials, such as the empirical trial and error
method and the density functional theory (DFT)‐based method, are unable to keep pace …

The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …

Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …

Physical and biogeochemical heterogeneity dramatically impacts fluid flow and reactive
solute transport behaviors in geological formations across scales. From micro pores to …

Deep neural network (DNN) exhibits state-of-the-art performance in many fields including
microstructure recognition where big dataset is used in training. However, DNN trained by …

Artificial intelligence (AI) and machine learning (ML) have been increasingly used in
materials science to build predictive models and accelerate discovery. For selected …