102 citations · 115 across the 7 of their papers we have counts for
7 papers
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…
Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization
Marcus Schwarting, Logan Ward, Nathaniel Hudson +5
Generative AI poses both opportunities and risks for solving inverse design problems in the sciences. Generative tools provide the ability to expand and refine a search space auton…
MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow
Xiaoli Yan, Nathaniel Hudson, Hyun Park +15
We present MOFA, an open-source generative AI (GenAI) plus simulation workflow for high-throughput generation of metal-organic frameworks (MOFs) on large-scale high-performance com…
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…
A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps
Tyler H. Chang, Jakob R. Elias, Stefan M. Wild +2
In order to deploy machine learning in a real-world self-driving laboratory where data acquisition is costly and there are multiple competing design criteria, systems need to be ab…
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…
Real-Time Streaming and Event-driven Control of Scientific Experiments
Jakob R. Elias, Ryan Chard, Maksim Levental +3
Advancements in scientific instrument sensors and connected devices provide unprecedented insight into ongoing experiments and present new opportunities for control, optimization,…