The performance of models in terms of mean absolute error (MAE) for the prediction of energies (kcal mol⁻¹) and forces (kcal mol⁻¹ Å⁻¹) of molecules at CCSD or CCSD(T) accuracy. We randomly select 50 snapshots of the training data as the validation set and average the performance of NewtonNet over four random splits to find standard deviations. Best results in the standard deviation range are marked in bold.

		sGDML	NequIP (l = 1)	NewtonNet
Aspirin	Energy	0.158	—	0.100 ± 0.007
	Forces	0.761	0.339	0.356 ± 0.019
Benzene	Energy	0.003	—	0.004 ± 0.001
	Forces	0.039	0.018	0.011 ± 0.001
Ethanol	Energy	0.050	—	0.049 ± 0.007
	Forces	0.350	0.217	0.282 ± 0.032
Malonaldehyde	Energy	0.248	—	0.045 ± 0.004
	Forces	0.369	0.369	0.285 ± 0.038
Toluene	Energy	0.030	—	0.014 ± 0.001
	Forces	0.210	0.101	0.080 ± 0.005