Artificial Intelligence in Scientific Discovery: Transforming Research Methodologies Across Disciplines

Abstract

Artificial intelligence (AI) has moved from a peripheral computational aid to a central instrument of scientific discovery, reshaping how researchers across disciplines generate hypotheses, design experiments, analyse data, and interpret results. This article offers a comprehensive, cross-disciplinary review of how machine learning, and deep learning in particular, is transforming research methodologies, synthesising landmark advances from the life sciences, chemistry and materials science, the formal and physical sciences, and the data-rich observational fields of earth and climate science. We organise the review around a central thesis: AI is not merely accelerating existing methods but reconfiguring the scientific method itself, augmenting the traditional hypothesis-driven paradigm with data-driven, generative, and increasingly autonomous modes of inquiry. Drawing on the flagship literature, we trace the protein-structure revolution catalysed by AlphaFold; the generative design and autonomous robotic synthesis of novel molecules and materials; the use of learning systems to guide mathematical intuition, discover algorithms, and control physical apparatus; and the emergence of neural weather and climate models that rival established numerical simulation. We further examine how AI is transforming the research workflow itself through automated literature synthesis, hypothesis generation, and the first generation of autonomous “self-driving” laboratories and large-language-model research agents. Alongside these advances, the review analyses the methodological hazards that accompany them—data leakage and a reproducibility crisis, the opacity of learned models, the risk of mistaking predictive accuracy for scientific understanding, and inequities of access to data and compute. We conclude that AI augments rather than replaces human scientists, that its responsible integration demands new norms of transparency and validation, and that the discipline-spanning convergence on shared learning methods is itself among the most consequential methodological shifts in the modern history of science.