Computational Studies of Prebiotic Chemistry at the Age of Machine Learning: From Recent Breakthroughs to Future Revolutions

Abstract

The emergence of life from abiotic chemical systems remains a central and unresolved question in natural sciences. Understanding how simple molecules gave rise to the first biomolecules and self‐sustaining reaction networks requires integrating scarce experimental data with advanced computational methods. Machine learning (ML) is rapidly transforming research in prebiotic chemistry. In particular, ML‐based interatomic potentials enable the exploration of complex reaction mechanisms at near‐quantum accuracy and reduced computational cost. Beyond individual reactions, ML methods can also accelerate the study of chemical reaction networks by predicting transition states, reaction outcomes, and kinetic parameters. Here, we highlight recent methodological and conceptual advances, illustrating how ML complements traditional quantum chemical approaches. We further discuss emerging strategies for integrating data‐driven models with experimental and theoretical frameworks, expanding the scope and efficiency of research into the chemical origins of life.