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随着实验室自动化程度的提高以及指导优化过程的新型机器学习工具的发展，近年来在材料科学领域兴起了材料加速平台。高通量实验平台目前支持快速合成程序、在线材料表征和工作流程并行化，便于批量采样。相应地，材料科学和化学领域中的许多科学和工程挑战需要满足多个目标和约束，这引起了人们对于寻找带约束多目标优化问题（cMOOPs）求解框架的兴趣。虽然高通量实验平台能够对cMOOPs进行更快、更广泛的采样，但出于操作和化学成本、可用的反应物数量有限、以及时间因素等多方面考虑，评估预算通常限制在102至103个样本之间。在处理冲突目标时，cMOOPs的复杂性更为明显，因为人们不仅需要找到一个全局最优，而且需要找到所有目标的最优集合，这被称为帕累托前沿（PF）。
Fig.1 | Closed-loop workflow of the EGBO-guidedHTEplatform.
来自新加坡南洋理工大学材料科学与工程学院的KedarHippalgaonkar教授团队，设计了一个进化引导的贝叶斯优化（EGBO）算法。该算法将选择压力与q-噪声期望超体积提升（qNEHVI）优化器相结合，不仅能够高效地求解PF，而且在更好地覆盖PF的同时，限制在不可行域中的采样。他们建立了一个全自动化的银纳米颗粒合成实验室，并利用EGBO方法解决了高质量合成、高产率、低种子使用率的挑战。他们证明，通过适当的约束处理，EGBO的性能相较于最先进的qNEHVI有所提升。此外，在各种合成的多目标问题中，EGBO展现出显著的超体积提升，揭示了选择压力与qNEHVI优化器之间的协同作用。他们还证明了EGBO能够很好地覆盖PF，并且具有相对较好的提出可行解的能力。
Fig.5 | Handling of input constraints using pre- and post-repair.
Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs
Andre K. Y. Low, Flore Mekki-Berrada, Abhishek Gupta, Aleksandr Ostudin, Jiaxun Xie, Eleonore Vissol-Gaudin, Yee-Fun Lim, Qianxiao Li, Yew Soon Ong, Saif A. Khan & Kedar Hippalgaonkar
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.