# A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research
## 3 Results
### AI Methodologies for Experimental Design
#### 3.2.1 Single Approaches  
Based on the above-mentioned AI methodologies, various frameworks were found that incorporated a single technique. This section discusses some example frameworks, categorized by AI classes. Optimization:  
Several frameworks found in the contributions were based on BO for optimizing experiments or process parameters and selecting new experiments accordingly in an iterative manner; the implementations were mostly similar. For example, an approach for single-objective BO was proposed by [PERSON] et al. [11]. Here, an ensemble neural network (ENN) surrogate model was used for predicting the in-flow synthesis of Cu-based, lead-based metal halide perovskite nanocrystals. As a decision policy and acquisition function, expected improvement (EI) was proposed by the authors. In contrast, [PERSON] et al. applied a combination of EI and predictive variance (PV) as an acquisition function to develop high-performance catalysts for higher alcohol synthesis, using a Gaussian process (GP) surrogate model. Regarding GAs, the framework presented by [PERSON] et al. [225] uses partial least squares regression (PLSR) and a GA for quality control in semiconductor manufacturing. The PLSR is applied to predict the experimental results and, thus, serves as a fitness function for the GA. Supervised Learning:  
[PERSON] et al. [221] proposed a supervised-based framework employing a random forest (RF) to predict the experimental or process output, aiming to optimize the extraction process of cannabinoids and predict the yields and extraction outcomes under different conditions. Thus, the selection of optimal process conditions and the exploration of unseen extraction yields could be achieved. The general framework \"Golem\" developed by [PERSON] et al. [217] utilizes a tree-based regression model as a surrogate model to estimate robustness against variability in experimental conditions, such as noise or changing laboratories. In combination with optimization methods such as BO or GA, it supports the optimization of conditions and the selection of robust experiments to achieve reproducible results. Another supervised learning approach presented by [PERSON] et al. [232] employs decision tree (DT) models to predict the experimental output of several synthesis conditions in terms of the formation of impurity phases in BiFeO3 thin-film synthesis. \begin{table}
\begin{tabular}{l c} \hline \hline
**Methodology** & **Number** \\ \hline BO & 9 \\ GA & 1 \\ GP regression & 1 \\ Tree-based regression & 2 \\ Tree-based classifier & 2 \\ Naive Bayes Classifier & 1 \\ LLM & 1 \\ LASSO regression & 1 \\ AL & 5 \\ RL & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Overview of methodologies used. Methodologies that are part of another methodology, such as regression surrogates in BO, are not considered separately. Active Learning:  
Various approaches have applied AL techniques to experimental processes. The experimental results of the obtained data are fed back to train the models, improving and optimizing the yield of the experimental output. [PERSON] et al. [222] proposed a framework for predicting the reaction yield and for selecting experiments and reactions, which are the most uncertain for the predictive model, for the reduction of reaction screening. A single-trained model with applied Monte Carlo (MC) dropout masks and an ENN is used to estimate uncertainty. The ENN performed slightly better, depending on the specific task. Similarly, [PERSON] et al. [229] presented an AL framework for reaction optimization, which predicts the reaction yield and estimates uncertainty, but it is based on RF regression. The AL methodologies were utilized to optimize reactions and concrete in a case study for optimizing a Pd-catalyzed C-Hylation reaction. To select experiments to perform, an exploration-exploitation strategy is employed based on prediction and uncertainty estimation.