High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

Large language models (LLMs) have emerged as powerful tools in chemistry, significantly
impacting molecule design, property prediction, and synthesis optimization. This review …

Transformer neural networks show promising capabilities, in particular for uses in materials
analysis, design, and manufacturing, including their capacity to work effectively with human …

X-ray diffraction (XRD) is a proven, powerful technique for determining the phase
composition, structure, and microstructural features of crystalline materials. The use of …

The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …

Discovering new materials is a challenging task in materials science crucial to the progress
of human society. Conventional approaches based on experiments and simulations are …

The idea of materials discovery has excited and perplexed research scientists for centuries.
Several different methods have been employed to find new types of materials, ranging from …

During the last few decades, industries such as aerospace and wind energy (among others)
have been remarkably influenced by the introduction of high-performance composites. One …

Renowned for their high porosity and structural diversity, metal–organic frameworks (MOFs)
are a promising class of materials for a wide range of applications. In recent decades, with …

With massive influx of new funding and emergence of modern facilities and centers, the area
of semiconductor manufacturing and processing has attained national and global …