# A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research
## 3 Results
### AI Methodologies for Experimental Design  
This section examines the machine learning and AI methodologies employed in the reviewed literature for experimental design, offering an overview of their implementations. For this study, the methodologies are grouped into four categories: optimization, supervised learning, active learning (AL), and reinforcement learning (RL). A sketch of these methodologies can be found in Figure 4.  
These categories were defined based on their primary functional objectives. Optimization refers to algorithms whose core purpose is to iteratively improve a target outcome but may not necessarily be efficient, such as Bayesian optimization (BO) and genetic algorithms (GAs). Supervised learning refers to models trained on labeled datasets to predict or classify outcomes, such as regression and classification models. AL incorporates methods that use models to select data points (experiments) that are most informative for improving the model. It aims to enhance the learning process by reducing the amount of data required for effective model training. RL encompasses methods in which agents learn optimal actions through interaction with an environment using reward signals.  
While there is an acknowledged conceptual overlap among these categories, particularly between optimization and active learning, this study adopted a function-oriented classification and did not aim to clearly distinguish the different classes using explicit definitions. Table 6 shows the number of AI techniques found in the reviewed literature under each category. The numbers in parentheses represent techniques used independently without being combined with other techniques.  
It can be observed that optimization and AL methodologies were used most frequently, especially as standalone approaches. In contrast, supervised techniques were combined with other methodologies for experimental design. By delving deeper into the methodologies to identify the utilized techniques, we have obtained the results shown in Table 7.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Category** & **Total Number (Separate)** \\ \hline Optimization & 10 (9) \\ Supervised & 8 (3) \\ AL & 5 (5) \\ RL & 2 (1) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Categorical breakdown of applied AI methodologies with their frequency of occurrence. Methodologies that are part of another methodology, such as regression surrogates in BO, are not considered separately. The numbers in parentheses stand for the number of methodologies used independently, without being combined with another methodology.  
Figure 4: An overview of the different AI classes, methodologies, and techniques used in this survey.  
The table shows that the most widely applied technique was BO, followed by regression and classification methods. Various AL methodologies were employed, among them machine learning and non-machine learning techniques, but each technique only occurred once.  
The implementation of the identified techniques is highlighted in the following by example frameworks. A distinction between single and hybrid approaches is made.