Fig. 6 Performance of the AIMNet model on several benchmark sets compared to MM, semiempirical, DFT, and ab initio QM methods. Performance of the AIMNet.

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Presentation transcript:

Fig. 6 Performance of the AIMNet model on several benchmark sets compared to MM, semiempirical, DFT, and ab initio QM methods. Performance of the AIMNet model on several benchmark sets compared to MM, semiempirical, DFT, and ab initio QM methods. (A and B) Torsion benchmark of Sellers et al. (29) for gas phase and solvent models. The dots correspond to mean absolute error (MAE) for each of the 62 torsion profiles for each method. (C) The dots correspond to absolute error for relative conformer energy for each of the 196 conformers in the dataset. (A to C) The boxes represent the upper and lower quartiles, while the whiskers represent 1.5 times the interquartile range or the minimum/maximum values. The methods are ordered in descending median errors from top to bottom. Boxes colored by the class of the computational method (ML, MM, SQM, DFT, and ab initio). The basis sets used to obtain reference energies are 6-311+G**, for (A) and (B), and def2-TZVPD, for (C), where applicable but not specified. (D) Correlation plot of experimental solvation energies for 238 neutral molecules from the MNSol database (31) and AIMNet predictions, calculated as the difference between the prediction of DFT and DFT(SMD) energies. Roman Zubatyuk et al. Sci Adv 2019;5:eaav6490 Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).