Machine Learning (Johannes and Alessandro)

From the point of view of the platform, there is no difference between running a quantum chemistry or a machine learning code. They both take as input a molecule and some parameters, and return a conforming output. The only difference is that machine learning code may not require the existence of a 3D structure, and can still operate when only line formats such as InChI or SMILES are available.
We have developed two images for codes that use machine learning to predict the result of a calculation: ANI and ChemML.
For the ANI image we have used the Pytorch implementation of the ANI potentials - TorchANI - and use it as an ASE calculator. We then simply leverage the algorithms in ASE to drive task such as geometry optimizations and normal modes calculations.
TorchANI image features: