3 citations · 6 across the 3 of their papers we have counts for
4 papers
Democratizing Atomistic Simulation Workflows for the AI Era with the Quantum Accelerator
Andrew S. Rosen, Naisargi Goyal, Brad Ayers +25
We present the Quantum Accelerator (QuAcc), an open-source workflow library for atomistic simulations with an emphasis on quantum-mechanical calculations. QuAcc provides predefined…
Experiences with Model Context Protocol Servers for Science and High Performance Computing
Haochen Pan, Ryan Chard, Reid Mello +12
Large language model (LLM)-powered agents are increasingly used to plan and execute scientific workflows, yet most research cyberinfrastructure (CI) exposes heterogeneous APIs and…
Machine Learning Materials Properties with Accurate Predictions, Uncertainty Estimates, Domain Guidance, and Persistent Online Accessibility
Ryan Jacobs, Lane E. Schultz, Aristana Scourtas +7
One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materia…
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
Nathaniel Hudson, J. Gregory Pauloski, Matt Baughman +13
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we…