  In the field of materials science, comprehending material properties is often
hindered by the complexity of datasets originating from various sources. This
study introduces the Automated Model Training (AMT) Graphical User Interface
(GUI), specifically crafted for the use of researchers and scientists without a
programming background to design the next set of experiments either in a
chemistry lab or on the computer. The GUI integrates diverse machine learning
models, such as XG-Boost, Random Forest, Support Vector Regression, Linear
Regression, Generalized Additive Model (GAM), and Stack Regressors, offering a
robust toolkit for data analysis. It facilitates the exploration of complex
relationships, non-linear patterns, and predictive accuracy optimization. To
further enhance its utility, the GUI integrates the Particle Swarm Optimization
(PSO) technique, allowing researchers to systematically explore vast parameter
spaces and identify optimal experimental conditions. This synergy between
machine learning and PSO empowers material scientists through a user-friendly
platform for data-driven discovery. The AMT GUI bridges the gap between
traditional experimentation and machine learning, enabling precise and
efficient exploration of the materials research space.
