1 citations · 1 across the 5 of their papers we have counts for
5 papers
PyOMES: an open-source framework for biochemical process modelling
Ethan Errington, Tom Vinestock, Jaewook Lee +1
PyOMES is a Python-based, Open-source Modelling Environment for (bio)chemical process Simulation that aims to simplify the modelling of dynamic (including steady state) processes.…
Transfer Learning Architectures for Scalable Multi-Fidelity Bayesian Optimization
Jaewook Lee, Ethan Errington, Christian D. Lorenz +1
Self-driving laboratories increasingly rely on multi-fidelity Bayesian optimization (MFBO) to balance cheap, approximate evaluations against scarce, expensive ones, with a predicti…
Comparison of Optimised Geometric Deep Learning Architectures, over Varying Toxicological Assay Data Environments
Alexander D. Kalian, Lennart Otte, Jaewook Lee +6
Geometric deep learning is an emerging technique in Artificial Intelligence (AI) driven cheminformatics, however the unique implications of different Graph Neural Network (GNN) arc…
Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges
Alexander D. Kalian, Jaewook Lee, Stefan P. Johannesson +3
The global demand for sustainable protein sources has accelerated the need for intelligent tools that can rapidly process and synthesise domain-specific scientific knowledge. In th…
A KAN-based Interpretable Framework for Process-Informed Prediction of Global Warming Potential
Jaewook Lee, Xinyang Sun, Ethan Errington +1
Accurate prediction of Global Warming Potential (GWP) is essential for assessing the environmental impact of chemical processes and materials. Traditional GWP prediction models rel…