This team is using exascale-ready open-source software to exploit advanced, high-level quantum chemical methods to study transition metal systems with non-trivial electronic structure features, then use the data generated to train a machine-learning model that circumvents known limitations of the methods. 

This project will pioneer advancements in federated learning by fostering more effective simulation and modeling techniques, addressing the critical challenges of scalability and resilience in distributed federated learning systems and the associated scientific workflows.

The research in this project will combine massively parallel computer simulations at the Frontier and Aurora supercomputers with modern, quantummechanical theories to understand photocatalysts with unprecedented accuracy and generate new design principles. 

The transformative science impact of this team's work is in harnessing the unprecedented power of extreme-scale quantum-accurate MD simulations on exascale Frontier and Aurora to predict novel physical phenomena and guide experiments towards observing them

With its successful deployment, this team's foundation model in neuroscience is anticipated to be revolutionary in various scientific disciplines, including neuroscience, medicine, and psychology

Using state-of-the-art computational tools developed by the Exascale Computing Project and the Nuclear Energy Advanced Modeling and Simulation Program on leadership-class computing facilities, this team sets out to use Computational Fluid Dynamics (CFD) to accurately predict turbulent fluid flow and heat transfer phenomena in a wide range of nuclear power applications. 

The goal of this project is to advance the DOE’s simulation capabilities in important carbon-free energy sectors, including nuclear, fusion, and wind. 

The successful outcome of this research will achieve new scientific outcomes that demonstrate the latest potential for HPC, simulation, and AI to hyper-enable engineers to solve humanity's largest challenges, and the business implications of our success in this effort are represented by the $30 trillion opportunity described above.

This research aims to address this long-standing issue through first-principles simulations, focusing on the prospects of long-lived hypermassive neutron stars (HMNSs) as potential engines for short GRBs (sGRBs). 

The impact of the project will help to develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens.

The enhanced understanding of the physics and the ML-based models developed during this project will help improve design, optimization and safety of advanced nuclear reactors and energy systems.

Results from this work will be included in a V&V database for future validation of HTGR simulation codes and could be used to improve the sub-grid scale models in low-order codes.

The results from this project will advance the development of carbon capture technologies essential for mitigating greenhouse gas emissions impacts on health, the economy, and the environment.

The simulations in this project will provide a first-ever view into the changes to the microstructure of iron as it goes through phase transformation and extreme deformation, detail at the atomic level directly comparable to the experimental X-ray absorption data and allowing a deep understanding of iron’s behavior under these extraordinary conditions.

The simulations in this project will be performed with both Nek5000 and NekRS expanding their application to the Research Reactor Conversion Program.