Low, A. K. Y., Mekki-Berrada, F., Gupta, A., Ostudin, A., Xie, J., Vissol-Gaudin, E., Lim, Y.-F., Li, Q., Ong, Y. S., Khan, S. A., Hippalgaonkar, K. (2024). Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs. Npj Computational Materials, 10(1). https://doi.org/10.1038/s41524-024-01274-x
Abstract:
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. However, current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints. Here, we devise an Evolution-Guided Bayesian Optimization (EGBO) algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not only solves for the Pareto Front (PF) efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space. The algorithm is developed together with a custom self-driving lab for seed-mediated silver nanoparticle synthesis, targeting 3 objectives (1) optical properties, (2) fast reaction, and (3) minimal seed usage alongside complex constraints. We demonstrate that, with appropriate constraint handling, EGBO performance improves upon state-of-the-art qNEHVI. Furthermore, across various synthetic multi-objective problems, EGBO shows significative hypervolume improvement, revealing the synergy between selection pressure and the qNEHVI optimizer. We also demonstrate EGBO’s good coverage of the PF as well as comparatively better ability to propose feasible solutions. We thus propose EGBO as a general framework for efficiently solving constrained multi-objective problems in high-throughput experimentation platforms.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Grant
Grant Reference no. : A1898b0043
This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Grant
Grant Reference no. : A20G9b0135
This research / project is supported by the National Research Foundation, Singapore - NRF Fellowship
Grant Reference no. : NRF- NRFF13-2021-0011
This research / project is supported by the National Research Foundation, Singapore - 25th NRF Competitive Research Programme
Grant Reference no. : NRF-CRP25-2020RS-0002
This research / project is supported by the National Research Foundation, Singapore - NRF Fellowship
Grant Reference no. : NRF- NRFF13-2021-0005
Ministry of Education, Singapore, under its Research Centre of Excellence award to the Institute for Functional Intelligent Materials (I-FIM)