Prediction of crystal structures with desirable material properties is a grand challenge in
materials research, due to the enormous search space of possible combinations of elements …

In real-world materials research, machine learning (ML) models are usually expected to
predict and discover novel exceptional materials that deviate from the known materials. It is …

The discovery of advanced thermal materials with exceptional phonon properties drives
technological advancements, impacting innovations from electronics to superconductors …

Materials property prediction models are usually evaluated using random splitting of
datasets into training and test datasets, which not only leads to over-estimated performance …

Electronic devices get smaller and smaller in every generation. In micro-/nano-electronic
devices such as high electron mobility transistors, heat dissipation has become a crucial …

Materials property prediction models are usually evaluated using random splitting of
datasets into training and test datasets, which not only leads to over-estimated performance …

Machine learning approaches have recently emerged as powerful tools to probe structure-
property relationships in crystals and molecules. Specifically, Machine learning interatomic …

Image denoising has been one of the important problems in the field of computer vision, and
it has a wide range of practical value in many applications, such as medical image …

With the rapid growth of non-customized data assets, how to assess their value accurately
and objectively has become the focus of current research. Existing studies have shown that …