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Accurate predictions of interatomic energies and forces are essential forhigh quality molecular dynamic simulations (MD). Machine learning algorithmscan be used to overcome limitations of classical MD by predicting ab initioquality energies and forces. SE(3)-equivariant neural network allow reasoningover spatial relationships and exploiting the rotational and translationalsymmetries. One such algorithm is the SE(3)-Transformer, which we adapt for theANI-1x dataset. Our early experimental results indicate through ablationstudies that deeper networks - with additional SE(3)-Transformer layers - couldreach necessary accuracies to allow effective integration with MD. However,faster implementations of the SE(3)-Transformer will be required, such as therecently published accelerated version by Milesi.

Learning Small Molecule Energies and Interatomic Forces with an Equivariant Transformer on the ANI-1x Dataset