A research team from Skoltech introduced a new method which takes advantage of machine learning for studying the properties of polycrystals, composites, and multiphase systems. It attained high accuracy, nearly as good as that of quantum-mechanical methods, which are only applicable to materials with less than a few hundred atoms. The new method also benefits from active learning on local atomic environments. The paper came out in the Advanced Theory and Simulation journal.
“Many industrial materials are synthesized as polycrystals or multiphase systems. They contain both a single crystal and amorphous components between single crystal grains. The large number of atoms makes it hard to calculate the properties of these systems using modern quantum-mechanical methods. Density functional theory can only be applied to materials with a few hundred atoms. To address the problem, we use a machine-learning approach based on Moment Tensor Potentials (MTP). These potentials have been also developed at Skoltech under the guidance of Professor Alexander Shapeev,” commented Faridun Jalolov, the leading author of the study and a Skoltech PhD student at the Materials Science and Engineering program.
As compared to other solutions, the authors see the potential of the new method in active learning on local atomic environments. When calculating a large structure with many hundreds of thousands of atoms, the MTP identifies which atom makes a mistake in the calculations, or is calculated incorrectly. The reason for this could be the limited training dataset, which prevents all possible system configurations from being considered. A local environment of this atom is then “cut out” and its energy calculated using quantum mechanics. Afterwards, the data is added back to the training set for further learning. As the on-the-fly learning progresses, the calculations continue until they come across another configuration that needs to be included in the training process. Other known machine-learning potentials cannot learn on small local parts of large structures, which limits their applicability and accuracy.