Conveners
Plenary Session 5: Machine Learning and High-Throughput Approaches for Advanced Materials Discovery
- Qi An (Department of Materials Science and Engineering, Iowa State University of Science and Technology)
One of the fundamental properties of semiconductors is their ability to support electric currents in the presence of electric and magnetic fields. These properties are described by transport coefficients such as drift and Hall electron and hole mobilities. During the past decade, there has been considerable progress in first-principles atomic-scale calculations of these coefficients by...
Two-dimensional (2D) materials have garnered significant attention due to their unique properties which result from their reduced dimensionality and quantum confinement. In this talk, I will present our recent research on the data-driven discovery of various architectures of atomically thin materials for photocatalytic splitting of water for generating clean hydrogen. One of the most daunting...
The development of new materials and manufacturing processes is increasingly driven by advances in Artificial Intelligence (AI). In materials science, evaluating the properties of candidate designs often relies on computationally intensive simulations, which can become impractical when exploring vast design spaces. Machine learning offers a powerful alternative: surrogate models can...
