Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories.
Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories.
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DOI：
10.1039/c8sc02239a
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发表时间：
2018-10-21
期刊：
影响因子：
8.4
通讯作者：
Aspuru-Guzik A
中科院分区：
文献类型：
--
作者：
Häse F;Roch LM;Aspuru-Guzik A
Chimera可以为实验或昂贵的计算提供多目标优化，其中评估是限制因素。寻找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，它影响着科学、工程和经济学。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低。我们提出Chimera作为多目标优化的通用成就标化函数，其中评价是限制因素。Chimera结合了先验标量化与词典编纂方法的概念，适用于任何一组n个未知目标。重要的是，它不需要对个人目标有详细的先验知识。使用不同的单目标优化算法，在多个已建立的多目标分析基准集上验证了Chimera的性能。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）用于有效能量传输的四色素激子系统的反设计。结果表明，Chimera使各种优化算法能够快速找到理想条件。此外，所提出的应用强调了嵌合体的可解释性，以确证裁剪系统参数的设计选择。
Chimera enables multi-target optimization for experimentation or expensive computations, where evaluations are the limiting factor. 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.
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