13 citations · 30 across the 7 of their papers we have counts for
7 papers
La Agente Óptima: Towards Agentic Self-Driving Laboratories
Marcel Müller, Jiaru Bai, Willi Gottstein +14
Self-driving laboratories (SDLs) combine automated experimentation with adaptive decision-making to accelerate scientific discovery. Their operation nevertheless often depends on h…
El Agente Gráfico: A Semantic Execution Runtime for Scientific Agents
Jiaru Bai, Abdulrahman Aldossary, Thomas Swanick +12
Large language models (LLMs) can plan scientific workflows and generate code, but these capabilities do not specify how scientific state is validated, transferred and recorded acro…
EGMOF: Efficient Generation of Metal-Organic Frameworks Using a Hybrid Diffusion-Transformer Architecture
Seunghee Han, Yeonghun Kang, Taeun Bae +5
Designing materials with targeted properties remains challenging due to the vastness of chemical space and the scarcity of property-labeled data. While recent advances in generativ…
The Rise of Generative AI for Metal-Organic Framework Design and Synthesis
Chenru Duan, Aditya Nandy, Shyam Chand Pal +17
Advances in generative artificial intelligence are transforming how metal-organic frameworks (MOFs) are designed and discovered. This Perspective introduces the shift from laboriou…
Harnessing Large Language Model to collect and analyze Metal-organic framework property dataset
Wonseok Lee, Yeonghun Kang, Taeun Bae +1
This research was focused on the efficient collection of experimental Metal-Organic Framework (MOF) data from scientific literature to address the challenges of accessing hard-to-f…
ChatMOF: An Autonomous AI System for Predicting and Generating Metal-Organic Frameworks
Yeonghun Kang, Jihan Kim
ChatMOF is an autonomous Artificial Intelligence (AI) system that is built to predict and generate metal-organic frameworks (MOFs). By leveraging a large-scale language model (GPT-…
Mining Insights on Metal-Organic Framework Synthesis from Scientific Literature Texts
Hyunsoo Park, Yeonghun Kang, Wonyoung Choe +1
Identifying optimal synthesis conditions for metal-organic frameworks (MOFs) is a major challenge that can serve as a bottleneck for new materials discovery and development. Trial-…