The current trend indicates that energy demand and supply will eventually be controlled by
autonomous software that optimizes decision-making and energy distribution operations …

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 …

Solutions to many of the world's problems depend upon materials research and
development. However, advanced materials can take decades to discover and decades …

As artificial intelligence (AI) advances, it is critical to give conventional electronics the
capacity to “think,”“analyze,” and “advise.” The need for intelligent, self-powered devices has …

Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …

Conspectus Machine learning has become a common and powerful tool in materials
research. As more data become available, with the use of high-performance computing and …

Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an
integrated infrastructure to accelerate materials discovery and design using density …

Extensive efforts to gather materials data have largely overlooked potential data
redundancy. In this study, we present evidence of a significant degree of redundancy across …

Due to their numerous effects on human health and the natural environment, water
contamination with heavy metals and metalloids, caused by their extensive use in various …

In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …