Volk Amanda A, Abolhasani Milad
Dept. of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA.
Nat Commun. 2024 Feb 14;15(1):1378. doi: 10.1038/s41467-024-45569-5.
With the rise of self-driving labs (SDLs) and automated experimentation across chemical and materials sciences, there is a considerable challenge in designing the best autonomous lab for a given problem based on published studies alone. Determining what digital and physical features are germane to a specific study is a critical aspect of SDL design that needs to be approached quantitatively. Even when controlling for features such as dimensionality, every experimental space has unique requirements and challenges that influence the design of the optimal physical platform and algorithm. Metrics such as optimization rate are therefore not necessarily indicative of the capabilities of an SDL across different studies. In this perspective, we highlight some of the critical metrics for quantifying performance in SDLs to better guide researchers in implementing the most suitable strategies. We then provide a brief review of the existing literature under the lens of quantified performance as well as heuristic recommendations for platform and experimental space pairings.
随着化学和材料科学领域自动驾驶实验室（SDLs）的兴起以及自动化实验的开展，仅依据已发表的研究为特定问题设计最佳自主实验室面临着巨大挑战。确定哪些数字和物理特征与特定研究相关是SDL设计的关键方面，需要采用定量方法来处理。即使在控制诸如维度等特征时，每个实验空间都有独特的要求和挑战，这些会影响最优物理平台和算法的设计。因此，诸如优化率等指标不一定能表明SDL在不同研究中的能力。从这个角度出发，我们强调了一些用于量化SDL性能的关键指标，以便更好地指导研究人员实施最合适的策略。然后，我们从量化性能的角度简要回顾了现有文献，并针对平台和实验空间的配对给出了启发式建议。
