Abstract: Embodiments relate to system and methods involving use of a technique for managing a database for producing a material composition having a thermodynamic phase. The technique can include: receiving a binary phase diagram for each material to be used as a component of a high-entropy alloy (HEA); using one or more active learning machine learning techniques for generating a feature, the feature including: a primary feature that is representative of a probability that an HEA will exhibit a solid solution phase and/or an intermetallic phase, and a physics-based feature that is representative of a factor related to formation of a desired intermetallic HEA phase; encoding the primary feature and the physics-based feature; generating an output representation of a HEA alloy composition and phase of a predicted materials composition; and selecting a HEA composition and phase that will meet a material design criterion.

Abstract: Embodiments relate to a system for predicting thermodynamic phase of a material. The system includes a phase diagram image scanning processing module configured to scan a binary phase diagram for each material to be used as a component of a high-entropy alloy (HEA). The system includes a feature computation processing module configured to generate a primary feature and an adaptive feature. The primary feature is representative of a probability that the HEA will exhibit a solid solution phase and/or an intermetallic phase. The adaptive feature is representative of a factor favoring formation of a desired intermetallic HEA phase. The system includes a prediction module configured to encode the primary feature and/or the adaptive feature with thermodynamic data associated with formation of HEA alloy phases to provide an output representation of the HEA alloy phases for a material under analysis.

Abstract: An amorphous magnetic alloy is presented. The alloy has the general formula: (Fe1-xCox)nMoaPbBcCdSie, wherein n is the atomic percent of iron and cobalt; x is the fraction of n; a, b, c, d and e are the atomic percent of molybdenum, phosphorous, boron, carbon and silicon respectively and n, x, a, b, c, d and e are defined by following relationship: 76?n?85; 0.05<x?0.50; 0?a?4; b?10; 0?c<d; and 0.1?e?2. Articles comprising the alloy and methods employing the alloy for making articles are also presented.

Abstract: An amorphous magnetic alloy is presented. The alloy has the general formula: (Fe1-xCox)nMoaPbBcCdSie, wherein n is the atomic percent of iron and cobalt; x is the fraction of n; a, b, c, d and e are the atomic percent of molybdenum, phosphorous, boron, carbon and silicon respectively and n, x, a, b, c, d and e are defined by following relationship: 76?n?85; 0.05<x?0.50; 0?a?4; b?10; 0?c<d; and 0.1?e?2. Articles comprising the alloy and methods employing the alloy for making articles are also presented.

Abstract: Bulk nanocrystalline Ti-based alloys were produced by conventional cooling from the corresponding liquid or high temperature solid phase followed by annealing at an appropriate temperature for a certain amount of time. The titanium-based alloys have a composition represented by the following formula, Ti.sub.a Cr.sub.b Cu.sub.c M.sub.d whereinM is at least one metal element selected from the group consisting of Mn, Mo, Fe.a, b, c, and d are atomic percentages falling within the following ranges:60<a<90, 2<b<20, 2<c<25, and 1<d<15.Generally, the titanium-based alloys are in a nanocrystalline state, sometimes coexisting with an amorphous phase. These titanium-based alloys are economically produced, free of porosity and high strength (twice as that of commercial alloys) with good ductility. Furthermore, these bulk nanocrystalline alloys can be made in large-sized ingots, thermally recycled and have good processability. These properties make these alloys suitable for various applications.