Also see my Google Scholar page.
Multi-objective Bayesian optimisation using q-noisy expected hypervolume improvement (q NEHVI) for the Schotten–Baumann reaction
J. Zhang, N. Sugisawa, K. Felton, S. Fuse, A. Lapkin, 2024
ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction
K. Felton, L. Rasßpe-Lange, J. Rittig ,K. Leonhard , A. Mitsos , J. Meyer-Kirschner, C. Knösche , A. Lapkin, 2023.
A Brief Introduction to Chemical Reaction Optimization
C.J. Taylor, A. Pomberger, K.C. Felton, R. Grainger, M. Barecka, T.W. Chamberlain, R.A. Bourne, C.N, Johnson, A.A. Lapkin, Chemical Reviews, 2023.
Accelerated Chemical Reaction Optimization using Multi-Task Learning
C. Taylor*, K. Felton*, D. Wigh, M. Jeraal, R. Grainger, G. Chessari, C. Johnson, A. Lapkin, 2023
* Joint first authors
DeepGamma: A deep learning model for activity coefficient prediction
K. Felton, H. Ben Safar, A.A. Lapkin, AAAI AI2ASE Workshop, 2022.
Optimization of Formulations Using Robotic Experiments Driven by Machine Learning DoE
L. Cao, D. Russo, K. Felton, D. Salley, A. Sharma, G. Keenan, W. Mauer, H. Gao, L. Cronin. A.A. Lapkin, Cell Physical Reports, 2021.
Multi-task Bayesian Optimization of Chemical Reactions
K. Felton, D. Wigh, A. Lapkin, NIPS ML4Molecules Workshop, 2020 (accepted).
Summit: Benchmarking Machine Learning for Reaction Optimisation
K. Felton*, J. Rittig*, A. Lapkin. Chemistry Methods, 2021.
* Joint first authors
A Modular Microfluidic Technology for Systematic Studies of Colloidal Semiconductor Nanocrystals
R. W. Epps, K. C. Felton, C. W. Coley, and M. Abolhasani. Journal of Visualized Experiments, 2018, e57666.
Automated Microfluidic Platform for Systematic Studies of Colloidal Perovskite Nanocrystals: Towards Continuous Nano-Manufacturing
R. W. Epps, K. C. Felton, C. W. Coley, and M. Abolhasani. Lab on a Chip, 2017, 17 (23), 4040-4047.
