Interatomic Interaction Models for Magnetic Materials:Recent Advances

Atomistic modeling is a widely employed theoretical method of computational materials science.It has found particular utility in the study of magnetic materials.Initially,magnetic empirical interatomic potentials or spinpolarized density functional theory(DFT)served as the primary models for describing interatomic interactions in atomistic simulations of magnetic systems.Furthermore,in recent years,a new class of interatomic potentials known as magnetic machine-learning interatomic potentials(magnetic MLIPs)has emerged.These MLIPs combine the computational efficiency,in terms of CPU time,of empirical potentials with the accuracy of DFT calculations.In this review,our focus lies on providing a comprehensive summary of the interatomic interaction models developed specifically for investigating magnetic materials.We also delve into the various problem classes to which these models can be applied.Finally,we offer insights into the future prospects of interatomic interaction model development for the exploration of magnetic materials.

Machine learning-driven synthesis of TiZrNbHfTaC_(5) high-entropy carbide

Synthesis of high-entropy carbides(HEC)requires high temperatures that can be provided by electric arc plasma method.However,the formation temperature of a single-phase sample remains unknown.Moreover,under some temperatures multi-phase structures can emerge.In this work,we developed an approach for a controllable synthesis of HEC TiZrNbHfTaC_(5) based on theoretical and experimental techniques.We used Canonical Monte Carlo(CMC)simulations with the machine learning interatomic potentials to determine the temperature conditions for the formation of single-phase and multi-phase samples.In full agreement with the theory,the single-phase sample,produced with electric arc discharge,was observed at 2000 K.Below 1200 K,the sample decomposed into(Ti-Nb-Ta)C,and a mixture of(Zr-Hf-Ta)C,(Zr-Nb-Hf)C,(Zr-Nb)C,and(Zr-Ta)C.Our results demonstrate the conditions for the formation of HEC and we anticipate that our approach can pave the way towards targeted synthesis of multicomponent materials.

Machine-learned multi-system surrogate models for materials prediction

Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the computational cost.We demonstrate surrogate models that simultaneously interpolate energies of different materials on a dataset of 10 binary alloys(AgCu,AlFe,AlMg,AlNi,AlTi,CoNi,CuFe,CuNi,FeV,and NbNi)with 10 different species and all possible fcc,bcc,and hcp structures up to eight atoms in the unit cell,15,950 structures in total.We find that the deviation of prediction errors when increasing the number of simultaneously modeled alloys is<1 meV/atom.Several state-of-the-art materials representations and learning algorithms were found to qualitatively agree on the prediction errors of formation enthalpy with relative errors of<2.5% for all systems.