A Perspective on Active Thermochemical Tables as Critical Data Infrastructure for Theory, Validation, and Artificial...

The Active Thermochemical Tables (ATcT) methodology represents a paradigm shift from traditional sequential thermochemistry to a network-based framework in which all available experimental and theoretical determinations are simultaneously incorporated and statistically reconciled within an overdetermined thermochemical network (TN). This perspective examines the role of ATcT as critical data infrastructure for theory development, method validation, and the emerging integration of artificial intelligence in the chemical sciences. We describe the structural limitations of legacy sequential thermochemical compilations and contrast them with the ATcT approach, which yields internally consistent thermochemical values with rigorously quantified uncertainties, full covariance structure, and quantitative provenance through variance decomposition. We present the first explicit formulation of edgewise uncertainty decomposition and leverage diagnostics within the context of thermochemical networks. The development of a RESTful API and an open-source Python client (atct) is described, providing machine-actionable, FAIR-compliant, and versioned programmatic access to ATcT data─including species correlations and covariance-aware reaction uncertainty propagation─capabilities essential for high-throughput benchmarking and automated computational workflows. We discuss the critical distinction between mean absolute deviation and 95% confidence intervals in method assessment, and the implications of using curated versus aggregated data for training machine learning models. The susceptibility of large language models to thermochemical hallucination is illustrated through the instructive case of the electron affinity of BH3, underscoring the necessity of coupling AI systems to authoritative, uncertainty-quantified data sources. ATcT's designation as a U.S. Department of Energy Office of Science Public Reusable Research (DOE SC PuRe) Data Resource ensures its long-term stewardship as foundational infrastructure for both traditional computational thermochemistry and next-generation AI-driven chemical research.