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Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories†
Chemical Science ( IF 8.4 ) Pub Date : 2018-08-28 00:00:00 , DOI: 10.1039/c8sc02239a
Florian Häse 1 , Loïc M Roch 1 , Alán Aspuru-Guzik 1, 2, 3, 4
Affiliation  

Finding the ideal conditions satisfying multiple pre-defined targets simultaneously is a challenging decision-making process, which impacts science, engineering, and economics. Additional complexity arises for tasks involving experimentation or expensive computations, as the number of evaluated conditions must be kept low. We propose Chimera as a general purpose achievement scalarizing function for multi-target optimization where evaluations are the limiting factor. Chimera combines concepts of a priori scalarizing with lexicographic approaches and is applicable to any set of n unknown objectives. Importantly, it does not require detailed prior knowledge about individual objectives. The performance of Chimera is demonstrated on several well-established analytic multi-objective benchmark sets using different single-objective optimization algorithms. We further illustrate the applicability and performance of Chimera with two practical examples: (i) the auto-calibration of a virtual robotic sampling sequence for direct-injection, and (ii) the inverse-design of a four-pigment excitonic system for an efficient energy transport. The results indicate that Chimera enables a wide class of optimization algorithms to rapidly find ideal conditions. Additionally, the presented applications highlight the interpretability of Chimera to corroborate design choices for tailoring system parameters.

中文翻译:

Chimera:为自动驾驶实验室实现基于层次结构的多目标优化†

找到同时满足多个预定义目标的理想条件是一个具有挑战性的决策过程,它会影响科学、工程和经济学。对于涉及实验或昂贵计算的任务会产生额外的复杂性,因为评估条件的数量必须保持较低。我们建议 Chimera 作为通用成就标量函数,用于多目标优化,其中评估是限制因素。Chimera 将先验标量化的概念与词典编纂方法相结合,适用于任何n 个未知目标的集合。重要的是,它不需要有关个人目标的详细先验知识。Chimera 的性能在使用不同单目标优化算法的几个完善的分析多目标基准集上得到了证明。我们通过两个实际例子进一步说明了 Chimera 的适用性和性能:(i)用于直接注射的虚拟机器人采样序列的自动校准,以及(ii)四颜料激子系统的逆向设计,以实现高效能源运输。结果表明,Chimera 使多种优化算法能够快速找到理想条件。此外,所提出的应用程序强调了 Chimera 的可解释性,以证实定制系统参数的设计选择。
更新日期:2018-08-28
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