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Computational design of ductile additively manufactured tungsten-based refractory alloys
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DOI：10.1016/j.commatsci.2026.114735.png)
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Abstract
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Tungsten exhibits exceptional temperature and radiation resistance, making it well-suited for applications involved in extreme environments. Further advancing part performance, additive manufacturing (AM) offers geometrical design freedom, repairability, and rapid prototyping capabilities, provided the intrinsic brittleness and low printability of tungsten can be overcome. Designing tungsten alloys with improved ductility (and thus printability in AM) can be accelerated by the use of a computationally-derived performance predictor to screen out brittle compositions. Calculations of the Pugh ratio by density functional theory (DFT) may serve this purpose well, given the value’s correlation with ductility. Further, this process can be made more efficient by the use of machine learning interatomic potentials (MLIPs) to accelerate DFT calculations. Here, we demonstrate that MLIPs can effectively identify optimal alloy compositions in the W–Ta–Nb system along the melting point–Pugh ratio Pareto front. The trend of the Pugh ratio as a function of tungsten fraction in the alloys is explained in terms of the electronic density of states at the Fermi level. Experimental validation reveals a strong correlation between the computed Pugh ratio and the observed crack fractions in additively manufactured alloys. Notably, the two alloys predicted to have the highest Pugh ratio values, W20Ta70Nb10 and W30Ta60Nb10, exhibit no intergranular micro-cracking in experiments.
Keywords:
Additive manufacturing
Printability
Refractory alloy
Elasticity
Machine learned potential
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