AI for Scientific Discovery: Statistical Methods in Physics, Biology, and Chemistry

Abstract

Artificial intelligence (AI) is increasingly reshaping the landscape of scientific discovery by enabling the integration of advanced statistical methods with large-scale, complex datasets across physics, biology, and chemistry. This study examines contemporary AI-driven statistical frameworks that facilitate hypothesis generation, pattern recognition, and predictive modeling in scientific research. Emphasis is placed on machine learning techniques such as Bayesian inference, deep learning, Gaussian processes, and probabilistic graphical models, which collectively enhance the capacity to model uncertainty, uncover hidden structures, and accelerate data-driven discovery. In physics, AI-based statistical methods are shown to improve the analysis of high-energy particle collisions, quantum systems, and astrophysical observations. In biology, these approaches support genomic sequencing, protein structure prediction, and systems biology modeling. In chemistry, AI enhances molecular design, reaction prediction, and materials discovery through data-intensive learning frameworks. The study highlights the interdisciplinary convergence of statistical theory and computational intelligence, demonstrating how AI not only augments traditional scientific methodologies but also enables entirely new paradigms of discovery. Despite significant progress, challenges remain in interpretability, data quality, and model generalization across domains. The paper concludes that continued integration of robust statistical principles with AI systems will be essential for advancing reliable, transparent, and reproducible scientific discovery across disciplines.