In data-intensive scientific fields—from materials science and bioinformatics to drug discovery and physics—the sheer volume and complexity of experimental data have become the primary bottleneck. Traditional manual analysis and trial-and-error experimentation are too slow to keep pace with modern data generation. The introduction of AI for Scientific Research and machine learning (ML) tools into the…