Design of Experiment Is All You Need: Utilizing AI for High-Entropy Alloy Water Splitting Catalyst
Authors: Kim Jihoon , Kang Jeung Ku 
Published: August 2024

High-entropy alloy (HEA) is a new group of material that have excited both fields of electrochemistry and computational chemistry, based on its intriguing concept and outstanding catalytic activity. Nevertheless, there exists a clear limitation to the 'trial-and-error' based research: its compositional space is too large. Therefore, AI-based high-throughput research is considered as an efficient way to optimize the composition of HEA electrocatalysts. To fully utilize AI's potential, the quality of data and rational Design of Experiment (DoE) is of importance. In this regard, we have 1) developed an automated one-step synthesis method of HEA with liquid handler and 2) made machine learning model with Gaussian process (GP) regression to minimize the overpotential (fig. 1a).

DoE helps developing a new experiment by categorizing the variables and steps that are necessary. First step of DoE is to start with listing the variables. For the one-step synthesis of HEAs, independent variable was the composition of each elements, where the control variables were:

A. Solvent (Amine/ethanol)

B. Addition of CNT (O/X)

C. Reaction temperature (high/low)

D. Reaction time (long/short)

E. Concentration of solution (5 mM / 10 mM)

Each of control variables have two options and the selection is represented by writing an alphabet. For example, if amine (A) and high reaction temperature (C) condition was selected while the second option is used for the rest, it is written as AC. Based on confounding method theory, by conducting AC experiment, we also get information about BDE experiment by assuming a negative correlation to each other. It is written as: AC = -BDE. Similarly, an equivalent combinatorial list can be made for every possible combination (=25) , while the number of experiment is reduced from 32 to 16. Among these combinations, ABCE was found to be optimal synthesis condition (fig.1b).

Afterward, we searched the optimal composition of HEAs, starting with grid searching the six-dimensional compositional field. MnFeCoNiCuMoPdPt - octonary alloy was the targeted alloy where the compositions of noble metal were fixed. Binomial distribution design was used to make the distance between neighboring points in 6-dimension compositional space equal (fig. 1c-d). 8C3 = 56 combinations were selected and consequently, the corresponding amounts of precursor solutions were transferred to 56 heat-inert vials and underwent heat treatment of 200 °C. Overpotential of 56 samples were measured and simple GP regression model was constructed based on these datasets.

GP regression is composed of two parts: exploration and exploitation. During exploration, the optimal combination is recommended by searching the point that has the largest deviation. Interestingly, by comparing the Shapley Additive explanations (SHAP) value of each elements, it is clearly proved that Cu was harmful and Ni was beneficial to water splitting performance. Thus, Cu was screened out during the end of the exploration step. After finishing exploration, the next combination was recommended by searching near the point with least overpotential (exploitation step). By using GP regression, the optimal composition among HEAs was discovered in less than 200 experiments. It showed the overpotential less than 30mV for HER and less than 250mV for OER, making the overall water-splitting potential less than 1.5 V. In summary, a GP regression model showed synergistic effect with DoE, and HEA with the optimal composition showed an outstanding water splitting performance, reducing overall overpotential by 50% compared to the first step.

Figure 1