Researchers from Skoltech and MIPT and their German, Austrian, and Norwegian colleagues have proposed and tested a new method for computer modeling of magnetic alloys. The method, which relies on machine learning, accurately predicted the energy, mechanical and magnetic characteristics of the alloy of iron and aluminum.
This has been made possible by accounting for the so-called magnetic moments of atoms that give rise to the effects of magnetism. The study is published in Scientific Reports and is a stepping stone toward modeling chromium nitride—an ultrahard and corrosion-resistant material used in metal forming, medical tools and implants.
Computer modeling of materials is often a balancing act between speed and accuracy. The gold standard for predicting material structure and properties with the least error is quantum mechanical calculations, such as solving the Schrodinger equation.
There are ways to accelerate these demanding computations, the most popular among them being density functional theory. The way DFT saves computation time is this: Rather than solve the equation with respect to the electron wave function, we find the so-called total electron density in the lowest energy state. However, even that only allows systems tens or hundreds of atoms large to be modeled on a supercomputer.
Larger systems require further simplification: Ignoring the electronic structure and considering so-called interatomic interaction potentials, which characterize the forces between atoms. Naturally, this sacrifices some accuracy in predicting a material’s properties.
Recent years have seen the rise of a new solution that offers the best of both worlds. It retains the accuracy of quantum mechanical calculations and drastically increases computation speed even for systems numbering thousands of atoms. One popular approach is to use machine learning to obtain interatomic potentials trained on quantum mechanical calculation results.
Such potentials give better predictions of material properties than their experimentally sourced analogs. However, machine learning interatomic potentials do not necessarily account for the magnetic moments of atoms, and this can cause errors in modeling magnetic materials.
To model the properties of such materials, a group of physicists and mathematicians from MIPT and Skoltech updated its Moment Tensor Potentials method for obtaining machine learning interatomic potentials, generalizing it to version mMTP. This new “magnetic” MTP has already been used to predict the energy of iron in its para- and ferromagnetic states. The new study in Scientific Reports applies the method to the two-component alloy of iron and aluminum.
Ivan Novikov, a senior research scientist at Skoltech and an associate professor at the MIPT Department of Chemical Physics of Functional Materials, commented, “Our team is developing machine learning potentials that speed up the quantum mechanical calculations needed to describe the properties of materials by approximately five orders of magnitude.
“Over the past three years, machine learning potentials with magnetic moment have been emerging, and we created our own mMTP and validated it on the system of iron. In the new paper, we sought to validate the potential on a two-component system and demonstrate the algorithm for building a dataset for training the potential.”