Solid State

Solid-state materials research focuses on understanding and predicting the properties of crystalline solids, aiming to accelerate the discovery of materials with desirable characteristics for various applications. Current research heavily utilizes machine learning, employing graph neural networks, transformer models, and self-organizing maps to predict properties like phonon density of states, proton conductivity, and formation energy, often leveraging large, multi-source databases. This computational approach significantly accelerates materials discovery, enabling the efficient screening of vast chemical spaces and the design of novel materials for energy storage, catalysis, and other critical technologies.