Computational Design of Ductile Additively Manufactured Tungsten-Based Refractory Alloys
Kareem Abdelmaqsoud, Daniel Sinclair, Venkata Satya Surya Amaranth Karra, S. Mohadeseh Taheri-Mousavi, Michael Widom, Bryan A. Webler, John R. Kitchin

TL;DR
This study uses computational methods and machine learning to identify tungsten-based alloy compositions with improved ductility for additive manufacturing, validated by experiments showing reduced cracking.
Contribution
It introduces a machine learning-accelerated computational approach to predict alloy ductility, enabling efficient design of tungsten alloys suitable for additive manufacturing.
Findings
Machine learning potentials effectively identify optimal alloy compositions.
Predicted high Pugh ratio alloys showed no microcracking experimentally.
Strong correlation between computed Pugh ratio and crack fraction observed.
Abstract
Tungsten exhibits exceptional temperature and radiation resistance, making it well-suited for applications in extreme environments such as nuclear fusion reactors. Additive manufacturing offers geometrical design freedom and rapid prototyping capabilities for these applications, provided the intrinsic brittleness and low printability of tungsten can be overcome. Designing tungsten alloys with improved ductility, and thus printability in additive manufacturing, can be accelerated using a computationally derived performance predictor to screen out brittle compositions. Calculations of the Pugh ratio using density functional theory may serve this purpose, given its correlation with ductility. This process can be made more efficient through the use of machine learning interatomic potentials to accelerate density functional theory calculations. Here, we demonstrate that machine learning…
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Taxonomy
TopicsFusion materials and technologies · Advanced materials and composites · Additive Manufacturing Materials and Processes
