1 citations · 1 across the 5 of their papers we have counts for
7 papers
Differential Learning for Robust Prediction of Thermal Stability with Application to Energetic Materials
Megan C. Davis, R. Seaton Ullberg, Jeremy N. Schroeder +5
Predicting thermal stability during handling and storage is essential for the design of safe and reliable energetic materials. However, experimental measurements vary significantly…
Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials
R. Seaton Ullberg, Megan C. Davis, Jeremy N. Schroeder +5
The discovery of new energetic materials is critical for advancing technologies from defense to private industry. However, experimental approaches remain slow and expensive while c…
Generative Chemical Language Models for Energetic Materials Discovery
Andrew Salij, R. Seaton Ullberg, Megan C. Davis +5
The discovery of new energetic materials remains a pressing challenge hindered by limited availability of high-quality data. To address this, we have developed generative molecular…
Unraveling Structure-Performance Trade-offs in Porous Transport Layers for PEM Water Electrolysis
Navneet Goswami, Sergio Diaz Abad, Jacob S. Spendelow +2
Scalable hydrogen production using proton exchange membrane water electrolyzers depends on overcoming efficiency losses arising from coupled multiphase, multicomponent transport an…
Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Functional Materials Discovery
Samuel Rothfarb, Megan C. Davis, Ivana Matanovic +3
Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We…
Hyperspectral Dual-Comb Compressive Imaging for Minimally-Invasive Video-Rate Endomicroscopy
Myoung-Gyun Suh, David Dang, Maodong Gao +6
Endoscopic imaging is essential for real-time visualization of internal organs, yet conventional systems remain bulky, complex, and expensive due to their reliance on large, multi-…
Design of Amine-Functionalized Materials for Direct Air Capture Using Integrated High-Throughput Calculations and Machine Learning
Megan C. Davis, Wilton J. M. Kort-Kamp, Ivana Matanovic +2
Direct air capture (DAC) of carbon dioxide is a critical technology for mitigating climate change, but current materials face limitations in efficiency and scalability. We discover…