Advances of machine learning in stable isotope geochemistry

Abstract

The rapid advancement of artificial intelligence (AI), particularly machine learning (ML), is revolutionizing stable isotope geochemistry, enhancing mass spectrometry-based analytical techniques and unlocking transformative capabilities in data interpretation and geochemical process modelling. This critical review offers a comprehensive synthesis of ML’s integrated applications within the field, encompassing a systematic survey of commonly employed algorithms—such as random forests, support vector machines, and neural networks—along with an in-depth examination of their geochemical uses. We classify and evaluate these methods, highlighting their roles in improving data processing efficiency, prediction accuracy, and mechanistic insight across diverse applications including provenance studies, paleoclimate reconstruction, and environmental forensics. Nevertheless, several pressing challenges impede broader implementation, such as data scarcity for non-traditional isotope systems, limited model interpretability, and the persistent risk of geochemically implausible predictions. We argue that overcoming the "black-box" nature of ML demands the integration of domain knowledge through physics-informed neural networks (PINNs), improved explainable AI (XAI) frameworks, and strengthened interdisciplinary collaboration. Looking ahead, we emphasize the need to optimize analytical accuracy through intelligent instrumentation, develop standardized data infrastructures, and foster algorithm innovation tailored to geochemical principles. This review aims to provide an authoritative reference by synthesizing recent advances, openly addressing current limitations, and outlining pragmatic research directions to accelerate the adoption of ML in stable isotope geochemistry. By tackling these priorities, ML stands to not only refine existing methodologies but also open new scientific frontiers in understanding Earth’s dynamic systems, ultimately revolutionizing isotope-enabled discovery.