NSF Funds Wissam Saidi for Computational Materials Research
Dr. Wissam Saidi, Associate Professor in the Department of Mechanical Engineering and Materials Science at the University of Pittsburgh, was selected to receive $600,000 of NSF funding over 3 years. The Saidi group develops and uses multiscale simulation tools, including force-field, density-functional theory, quantum Monte Carlo and quantum chemistry methods, to understand, predict, and design novel materials for applications in energy conversion and storage, surfaces and interfaces, spectroscopy, and nanoparticles.
The goal of the proposal, "DeepPDB: An open-source active-learning framework to enable high-fidelity atomistic simulations in unexplored material space", will be to offer an open-source toolkit with the ability to automatically generate estimates of force-fields parameters using advanced empirical-based computational tools. They will also curate and disseminate a validated repository of first-principles datasets and their corresponding potentials for inorganic materials. DeepPDB will serve both the materials science and machine learning communities, by providing the former with critical parameters to solve materials challenges and the latter by benchmark datasets for machine learning development. The resulting synergy will enable artificial intelligence and machine learning to play a greater role in computing critical materials properties for next-generation challenges. DeepPDB will also serve a critical educational objective, allowing the budding of a new generation of materials scientists, who understand how deep learning can be used to solve materials science challenges.
Wissam is seeking a postdoc for this computational materials project, please see this description for more details and how to apply.