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Fe-B-Si system is a matrix for synthesis of new functional materials withexceptional magnetic and mechanical properties. Progress in this area isassociated with the search for optimal doping conditions. This theoretical andexperimental study is aimed to address the influence of Ta alloying on thestructure of undercooled (Fe0.75B0.15Si0.1)100-xTax (x=0-2) melts, theirundercoolability and the processes of structure formation duringsolidification. Small concentration of Ta complicates standard ab initio andmachine learning investigations. We developed a technique for fast and stabletraining of machine learning interatomic potential (MLIP) in this case anduncovered the structure of the undercooled melts. Molecular dynamic simulationswith MLIP showed that at Ta concentration of 1 at.% there is a sharp change inthe chemical short-range ordering in the melt associated with a change in theinteraction of Ta atoms. This effect leads to a restructuring of the clusterformation in the system. At the same time, our experimental investigation showsthat melts with a Ta content of 1 at.% have the greatest tendency toundercoolability. Alloying with Ta promotes the formation of primary crystalsof Fe2B, and at a concentration of more than 1.5 at.% Ta, also of FeTaB.Herewith, near 1 at.% Ta, the crystallization of the melt proceedsnontrivially: with the formation of two intermediate metastable phases Fe3B andFe2Ta Laves phase. Also, the highest tendency to amorphization under conditionsof quick quenching is exhibited by a melt with a Ta concentration of 1 at.%.The results not only provide understanding of optimal alloying of Fe-B-Simaterials but also promote a machine learning method for numerical design ofmetallic alloys with a small dopant concentration.

Structure and solidification of the (Fe0.75B0.15Si0.1)100-xTax (x=0-2) melts: experiment and machine learning