The Highest Melting Point Material: Searched by Bayesian Global Optimization with Deep Potential Molecular Dynamics
Fu-Zhi Dai, Bo Wen, Xingjian Jiao, Lei Chen, Yujin Wang

TL;DR
This study employs Bayesian global optimization with deep potential molecular dynamics to identify Hf-C-N compounds with the highest melting points, revealing new compositions and mechanisms to enhance refractory material performance for hypersonic vehicle applications.
Contribution
First, trained a deep potential model for Hf-Ta-C-N system; then used it with Bayesian optimization to discover the highest melting point material, introducing N solid solution as an effective enhancement.
Findings
Predicted melting points match experimental data.
N addition significantly increases melting point (~4236 K).
N addition reduces liquid phase stability, raising melting point.
Abstract
The interest in refractory materials is increasing rapidly in recent decades due to the development of hypersonic vehicles. However, which substance has the highest melting point keeps a secret, since precise measurements in extreme condition are overwhelmingly difficult. In the present work, an accurate deep potential model of Hf-Ta-C-N system was firstly trained, and then applied to search for the highest melting point material by using molecular dynamics simulation and Bayesian global optimization. The predicted melting points agree well with experiments, and confirm that the carbon site vacancy can enhance melting points of rock-salt structure carbides. Solid solution with N is verified as another new and more effective melting point enhancing approach for HfC, while the conventional routing of solid solution with Ta (e.g. HfTa4C5) is not suggested to result in a maximum melting…
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Taxonomy
TopicsAdvanced materials and composites · Metal and Thin Film Mechanics · Energetic Materials and Combustion
