102 citations · 183 across the 13 of their papers we have counts for
4 papers · 1 filter
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…
From Atomistic Models to Machine Learning: Predictive Design of Nanocarbons under Extreme Conditions
Xiaoli Yan, Millicent A. Firestone, Murat Keceli +2
The formation of technologically valuable nanocarbon structures under extreme conditions, such as those produced during high-explosive detonations, remains poorly understood but ho…
A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture
Hyun Park, Xiaoli Yan, Ruijie Zhu +5
Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view…
End-to-end AI framework for interpretable prediction of molecular and crystal properties
Hyun Park, Ruijie Zhu, E. A. Huerta +3
We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inferen…