Structure and solidification of the (Fe0.75B0.15Si0.1)100-xTax (x=0-2) melts: experiment and machine learning
I.V. Sterkhova, L.V. Kamaeva, V.I. Ladyanov, and N.M. Chtchelkatchev

TL;DR
This study combines experimental and machine learning methods to investigate how small amounts of tantalum affect the structure, solidification, and properties of Fe-B-Si melts, revealing optimal doping conditions for functional magnetic materials.
Contribution
It introduces a novel machine learning interatomic potential training technique for small dopant concentrations and uncovers structural changes and solidification behavior in Ta-doped Fe-B-Si melts.
Findings
Ta at 1 at.% causes a sharp change in short-range order.
Melt with 1 at.% Ta shows highest undercoolability.
Formation of metastable phases during crystallization at 1 at.% Ta.
Abstract
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…
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Taxonomy
TopicsMetallic Glasses and Amorphous Alloys · Solidification and crystal growth phenomena · Microstructure and Mechanical Properties of Steels
