MACE-OFF series (https://doi.org/10.1021/jacs.4c07099) are now available on Aitomistic Hub. You can request the calculation with MACE-OFF24 via input file: In python script, you can define the method as: The available method names are: It’s coming soon in the next release …
If you feel lost in the ocean of various datasets to train your machine learning potential, a comprehensive overview can serve as the compass to help navigate through the resources available. Recently, we published a review on Machine Learning: Science …
MLST: Review on data sets for molecular ML potentials Read more »
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? Here, we propose the all-in-one (AIO) ANI model, …
All-in-one: Learning across quantum chemical levels. Better than transfer learning! Read more »
Recently, the Chung group at Southern University of Science and Technology (SUSTech) has combined efficient machine learning potentials (MLPs) with multi-scale quantum refinement methods to enhance computational efficiency and reliability. Their results are published in Nature Communications. The CC-quality AIQM1 method in MLatom@XACS software was …