While methods like BOB/BRR and BOB/KRR had the second and fifth-lowest median MAPE, their ability to predict the geometry with the lowest energy, a repulsive wall, and attractive forces was quite poor compared to the other top methods based on MAPE. Other methods utilizing RFR also performed poorly, often predicting stepwise energy surfaces seen in Figure , thus being incapable of consistently predicting r 0 , attractive, or repulsive forces. This is seen in Figure when the bond breaking causes the only change in the ECFP representation and leads to the higher energy. Other ML methods such as ANI-1x, ANI-2x, FCHL, and Colorful CNN were able to accurately predict energies while also predicting the repulsive and attractive forces of the molecule. In short, while random forest methods may have accuracy at single-point properties, they prove inherently inaccurate for potential energy and should be avoided. A possible advantage for the ANI-1x and ANI-2x models is that some molecules in our test evaluation are found in the ANI-1x training set. In the training of the other methods, molecules in our test set were purposefully left out of the training set but may be present in the ANI-1x and ANI-2x model. For that reason, we will focus the remainder of our discussion on molecules outside of the ANI-1 training set, examining the best overall performers, ANI-1x, ANI-2x, FCHL, and Colorful CNN from Table . The performance of all methods is included in the supplemental information. As an example, further evaluations were carried out on energy predictions from frozen-rotor dihedral angle scans performed with !B97X 6-31G(d) for biphenyl and sucrose. Table compiles the predicted lowest energy angle for these molecules as well as the barrier energies from 45 to 0 for biphenyl and 0 to 60 for sucrose.