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 …

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

The exponential growth and success of machine learning (ML) has resulted in its application
in all scientific domains including material science. Advancement in experimental …

Deep learning-based models, while highly effective for prognostics and health management,
fail to reliably detect the data unknown in the training stage, referred to as out-of-distribution …

In this perspective paper, we introduce the concept of Specialized Generalist Artificial
Intelligence (SGAI or simply SGI) as a crucial milestone toward Artificial General Intelligence …

Artificial intelligence (AI) is rapidly emerging as a enabling tool for solving complex materials
design problems. This paper aims to review recent advances in AI‐driven materials‐by …

Achieving high accuracy on data from domains unseen during training is a fundamental
challenge in domain generalization (DG). While state-of-the-art DG classifiers have …

The rapid development of biosensing technologies together with the advent of deep learning
has marked an era in healthcare and biomedical research where widespread devices like …

This paper explores the crucial yet challenging task of stochastic nonlinear dynamic
buckling investigations of the GPLR-FGP plate under biaxial impacts in thermal …

Building machine learning models using a dataset calculated by first principles calculations
is an important approach to explore the next-generation batteries. In previous studies, the …