CCSDT in focal point approaches, that the difference between DLPNO-CCSDT and canonical CCSD(T) should be used rather than the difference between DLPNO-CCSDT and DLPNO-CCSD(T). Finally, to highlight the capabilities of our code, we performed timings on a growing series of linear alkanes as well as water clusters (geometries available in the supplementary material), depicted in Figures and. Results are presented using both the cc-pVDZ and cc-pVTZ basis sets, with the per-iteration DLPNO-CCSDT wall time (in minutes) evaluated with respect to the number of basis functions. For the alkanes, system sizes range from 1 to 30 carbons for cc-pVDZ (34 to 730 basis functions) and 1 to 12 carbons for cc-pVTZ ( to 724 basis functions). For the water clusters, the system sizes range from 4 to 131 water molecules using cc-pVDZ (96 to 3144 basis functions) and 4 to 49 waters using cc-pVTZ (232 to 2842 basis functions). In these computations, the same parameters are used as the pericyclic reactions. As shown in Figures and, the onset of linear scaling starts to occur in the range of these system sizes. To measure this, we performed a log-log regression of the per-iteration wall time with the number of basis functions, and fit the data to the function t = a • N b , where t is the run-time, a is a pre-factor, N the number of basis functions, and determined b (the observed scaling). For the linear alkanes, the observed scaling is around O(N 1.28 ) for 12-30 carbon atoms in cc-pVDZ (R 2 = 99.92%) and O(N 1.23 ) for 10-12 carbons in cc-pVTZ (R 2 = 99.90%). For the water clusters ranging from 34 to 131 molecules in the cc-pVDZ basis set, the observed scaling is O(N 1.30 ), and O(N 1.40 ) for 34-49 waters in the cc-pVTZ basis set. The R 2 values are 99.94% across both basis sets. This gives evidence to support that our algorithm is asymptotically linear-scaling, given sufficiently large systems.