1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…
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…
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…