Data-Driven Exploration of Synthesizable Strained Hydrocarbons as High-Energy-Density Candidates for Sustainable Aviation Fuels

Abstract

This study explores the chemical space of strained polycyclic hydrocarbons to identify novel candidates for high-energy-density sustainable aviation fuels (SAFs), with additional applications as 3D building blocks for natural product synthesis. Leveraging the AIQM2 machine-learning potential—which achieves CCSD(T)/CBS-level accuracy—we developed an extensive database of novel hydrocarbon architectures. High-throughput screening identified 20 novel candidates (up to C₁₀) specifically optimized for aviation fuel, characterized by 3-, 4-, and 5-membered saturated rings that significantly enhance energy density. Furthermore, the screening identified 16 unique cage-like scaffolds as potential building blocks for advanced synthesis. The discovery process integrated rigorous AI-based assessments of synthesizability and adherence to ASTM D7566/D4054 standards to ensure performance viability. This work demonstrates an automated, high-precision workflow for the targeted exploration of chemical space, offering a robust platform for discovering diverse, application-specific functional materials.