  Fe-B-Si system is a matrix for synthesis of new functional materials with
exceptional magnetic and mechanical properties. Progress in this area is
associated with the search for optimal doping conditions. This theoretical and
experimental study is aimed to address the influence of Ta alloying on the
structure of undercooled (Fe0.75B0.15Si0.1)100-xTax (x=0-2) melts, their
undercoolability and the processes of structure formation during
solidification. Small concentration of Ta complicates standard ab initio and
machine learning investigations. We developed a technique for fast and stable
training of machine learning interatomic potential (MLIP) in this case and
uncovered the structure of the undercooled melts. Molecular dynamic simulations
with MLIP showed that at Ta concentration of 1 at.% there is a sharp change in
the chemical short-range ordering in the melt associated with a change in the
interaction of Ta atoms. This effect leads to a restructuring of the cluster
formation in the system. At the same time, our experimental investigation shows
that melts with a Ta content of 1 at.% have the greatest tendency to
undercoolability. Alloying with Ta promotes the formation of primary crystals
of 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 proceeds
nontrivially: with the formation of two intermediate metastable phases Fe3B and
Fe2Ta Laves phase. Also, the highest tendency to amorphization under conditions
of 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-Si
materials but also promote a machine learning method for numerical design of
metallic alloys with a small dopant concentration.
