The explosion of quantum chemical datasets (see our overview of them) satisfies the appetite of those data-hungry machine learning potentials while raising another critical question: how to learn data in different fidelities?
Another advantage of AIO is its combination with the delta-learning strategy, where any pairs of corrections from low level to high level can be obtained, as long as AIO has already seen them.
More
detailed discussions can be found in our preprint at ChemRxiv.
The code and the foundational models are available at https://github.com/dralgroup/aio-ani. They will be integrated into the universal and
updatable AI-enhanced QM (UAIQM) library and made available in the MLatom
package so that they can be used online at the XACS cloud computing platform.