Material Descriptor

Material descriptors are numerical or symbolic representations of materials' properties and structures, crucial for accelerating materials discovery and optimization through data-driven methods. Current research focuses on developing robust descriptors that capture complex relationships, employing machine learning models like random forests, graph neural networks, and cluster expansion, often combined with feature selection techniques to improve efficiency and accuracy, particularly with limited datasets. These advancements enable more accurate predictions of material properties, such as energetics and dielectric functions, facilitating the design of novel materials with desired characteristics and reducing reliance on computationally expensive first-principles calculations.