Benchmarking Isomerization Energies for C 5 $$ {\mathrm{C}}_5 $$ - C 7 $$ {\mathrm{C}}_7 $$ Hydrocarbons: The ISOC7 Database.

Highly accurate benchmark databases are critical for the development of robust and computationally efficient electronic structure methods. We introduce the ISOC7 database, a diverse collection of 1308 unique constitutional isomers of C 5 $$ {\mathrm{C}}_5 $$ - C 7 $$ {\mathrm{C}}_7 $$ saturated and unsaturated hydrocarbons with reference isomerization energies at the CCSD(T)/CBS level of theory, obtained via the W1-F12 thermochemical protocol. The isomerization energies in this dataset span over 146  kcal mol - 1 $$ \mathrm{kcal}\kern0.3em {\mathrm{mol}}^{-1} $$ . This database was used to conduct a rigorous benchmark assessment of a wide hierarchy of computational methods. The performance of 40 contemporary density functional theory (DFT) functionals reveals a general, albeit not monotonic, improvement along the rungs of Jacob's Ladder, with lower-rung GGA and MGGA functionals providing generally poor performance. The range-separated hybrid-meta-GGA functional ω $$ \omega $$ B97M-D4 emerges as the top DFT performer with a root-mean-square deviation (RMSD) of 1.62  kcal mol - 1 $$ \mathrm{kcal}\kern0.3em {\mathrm{mol}}^{-1} $$ . We also evaluated computationally economical semiempirical and tight-binding methods. While traditional semiempirical approaches are inadequate, the modern g-xTB tight-binding method achieves a respectable RMSD of 4.14  kcal mol - 1 $$ \mathrm{kcal}\kern0.3em {\mathrm{mol}}^{-1} $$ . Remarkably, the machine-learned neural network potential AIMNet2 delivers exceptional accuracy, achieving an RMSD of 1.67  kcal mol - 1 $$ \mathrm{kcal}\kern0.3em {\mathrm{mol}}^{-1} $$ , rivaling the performance of the best DFT functional at a fraction of the computational cost. The ISOC7 database provides a challenging benchmark for advancing the development and validation of both quantum chemical and machine learning methods.