# A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research
## 3 Results
### Degree of Automation  
In this section, the degree of automation of the proposed frameworks is investigated. The contributions are categorized into three classes: supportive, partially autonomous, and fully autonomous.  
Within the scope of this work, a fully autonomous framework is defined as one that independently plans, selects, executes, and analyzes experiments while autonomously integrating experimental outcomes to iteratively improve its strategies. Such systems operate without any requirement for human intervention, achieving an entirely closed-loop experimental design process.  
In contrast, partially autonomous frameworks perform multiple steps in the fully autonomous workflow and incorporate feedback from previous experiments. However, these frameworks do not include an automated execution and analysis stage. Partially autonomous frameworks still depend on humans to execute experiments and make decisions at critical stages, such as approving experimental suggestions or interpreting results. In the literature, these frameworks are termed \"human-in-the-loop\" systems, in which automation supports but does not fully replace human expertise.  
Supportive frameworks provide assistance by proposing experimental candidates, narrowing the search space, or optimizing specific aspects of the experimental process. They do not autonomously adjust their strategies based on experimental outcomes and lack an iterative feedback mechanism. The final decision-making and execution processes remain entirely under human control. The classified contributions can be found in Table 4 and Figure 6, while the number of papers per automation category can be found in Table 9.  
The figure shows that partially autonomous frameworks are predominantly employed. Fully autonomous and supportive frameworks are underrepresented.  
Without an iterative optimization process and an autonomous experimentation platform, the objective of supportive frameworks is to model existing experimental data and predict results, making processes more efficient. Here, supervised learning techniques are employed. Using the results of experiments to optimize the models would make these frameworks partially autonomous.  
Partially autonomous frameworks typically utilize AL or optimization techniques due to their iterative optimization processes. By combining an autonomous experimentation platform with a partially autonomous framework, a fully autonomous framework can be achieved.  
Fully autonomous frameworks are achieved in the context of SDLs [PERSON] et al. (2011), [PERSON] et al. (2019), [PERSON] et al. (2020). BO, AL, and RL/supervised learning are applied as AI techniques. The techniques are combined with an autonomous experimentation platform for executing and analyzing experiments. [PERSON] et al. (2011) utilized a self-developed modular platform, consisting of fluid delivery, mixing and reaction, and in situ characterization. [PERSON] et al. (2019) applied IBM's RoboRXN platform and used robotic actions by RoboRXN for executing experiments and HLPC for analyzing experimental results. [PERSON] et al. (2020) also employed a self-developed modular platform, incorporating an autonomous LHP QD synthesis bot. In the context of carbon nanotube growth, an aged  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Automation** & **Number** \\ \hline Fully autonomous & 5 \\ Partially autonomous & 13 \\ Supportive & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The number of papers per automation category.  
walled, supported catalyst chemical vapor deposition system was used by [PERSON] et al. The system was integrated into the ARES (Automated Research System) platform.