The design space for nanomaterials is vast, and traditional trial-and-error methods can be time-consuming and inefficient. For most devices and technologies, the careful design of material properties through methods such as alloying, creating heterostructures or composites, or controlled introduction of defects is necessary. However, the potential configuration space of such design elements can be extremely large, making it impractical to explore each possibility one by one.
Therefore, a team of researchers from the National University of Singapore’s Institute for Functional Intelligent Materials are tapping onto the computational power of NSCC’s resources to carry out high-throughput first-principle calculations, resulting in the generation of a large database of materials. They will then structure the datasets and construct descriptors and representations before applying state-of-the-art machine learning methods to train models that can predict the physical properties of materials based on the input data. This will ultimately allow them to accelerate the discovery and development of new materials with desired properties.
To find out more about how NSCC’s HPC resources can help you, please contact [email protected].
NSCC NewsBytes April 2023
