论文标题
TizrnBHFTAC5高入脑碳化物的机器学习驱动的合成
Machine-learning Driven Synthesis of TiZrNbHfTaC5 High-Entropy Carbide
论文作者
论文摘要
高渗透碳化物（HEC）的合成需要通过电弧血浆法提供的高温。但是，单相样品的形成温度仍然未知。此外，在某些温度下，多相结构可能会出现。在这项工作中，我们开发了一种基于理论和实验技术的HEC TIZRNBHFTAC5可控合成的方法。我们使用了机器学习间势来确定形成单相和多相样品的温度条件，我们使用了典型的蒙特卡洛（CMC）模拟。与该理论完全一致，在2000 K下观察到了用电弧排出产生的单相样品。在1200 K以下的样品以下样品分解为（Ti-NB-TA）C和（ZR-HF-TA）C的混合物，（ZR-NB-HF）C，（ZR-NB-HF）C，（ZR-NB）C，（ZR-NB）C，和（ZR-TA）C。我们的结果证明了HEC形成的条件，我们预计我们的方法可以为靶向多组分材料的靶向合成铺平道路。
Synthesis of high-entropy carbides (HEC) requires high temperatures that can be provided by electric arc plasma method. However, the formation temperature of a single-phase sample remains unknown. Moreover, under some temperatures multi-phase structures can emerge. In this work we developed an approach for a controllable synthesis of HEC TiZrNbHfTaC5 based on theoretical and experimental techniques. We used canonical Monte Carlo (CMC) simulations with the machine learning interatomic potentials to determine the temperature conditions for the formation of single-phase and multi-phase samples. In full agreement with the theory, the single-phase sample, produced with electric arc discharge, was observed at 2000 K. Below 1200 K the sample decomposed into (Ti-Nb-Ta)C and a mixture of (Zr-Hf-Ta)C, (Zr-Nb-Hf)C, (Zr-Nb)C, and (Zr-Ta)C. Our results demonstrate the conditions for the formation of HEC and we anticipate that our approach can pave the way towards targeted synthesis of multicomponent materials.
