Session

DescriptionThe design and discovery of materials with desired functional properties is challenging due to labor-intensive experimental measurements and computationally expensive physics-based models, which preclude a thorough exploration of large chemical spaces characterized by several chemical compositions and atomic configurations per composition. This disconnect has motivated the development of data-driven surrogate models that can overcome experimental and computational bottlenecks to enable an effective exploration of such vast chemical spaces. In this minisymposium, we discuss new generative artificial intelligence (AI) methods to perform materials design. A particular advantage of generative AI approaches is their ability to learn the context and syntax of molecular data described by fundamental principles of physics and chemistry, providing a critical basis for informing the generative design of molecules. In order to ensure generalizability and robustness of the generative model, the generative AI model needs to be trained on a large volume of data that thoroughly samples diverse chemical regions. Due to the large volumes of data that must be processed, efficiently training these models requires leveraging a massive amount of high performance computing (HPC) resources for scalable training. This minisymposium aims to broadly cover HPC aspects for scalable generative AI models across several heterogeneous distributed computational environments.