材料智能计算在多主元超高温金属陶瓷设计中的应用进展
Progress in Application of Materials Intelligent Calculation in the Design of Multi-principal Component Ultra-high Temperature Ceramics
林洋 1卜文刚 2何鹏飞 2张志彬 2梁秀兵 2种晓宇1
作者信息
- 1. 昆明理工大学材料科学与工程学院,昆明 650093
- 2. 军事科学院国防科技创新研究院,北京 100071
- 折叠
摘要
借鉴高熵合金设计思路而开发的高熵碳化物是一种新型超高温陶瓷材料,其具有独特的性能组合,包括高硬度、低热导率、高熔点、高强度和良好的抗辐照性等优异的物理性质,在先进涡轮发动机、核反应堆和高超音速飞行器等极端服役环境中具有广泛应用的潜力.高熵碳化物的优异物理性能来源于其复杂的元素成分组成、长程无序的原子结构、晶格畸变及空位缺陷对声子散射过程的增强等因素.然而,由于高熵碳化物的候选成分复杂,元素含量的变化范围广泛,导致传统"试错法"难以快速获得具有目标性能的高熵碳化物的组分范围.本文综述了第一性原理计算和机器学习在高熵碳化物设计的应用,着重回顾了高通量计算和机器学习在高熵碳化物相稳定性、力学性能及热力学性能预测中的研究进展,对相关计算方法在新型高熵陶瓷设计中的应用进行了展望.
Abstract
Inspired by the design principles of high-entropy alloys,high-entropy carbides have been developed,emerging as innovative ultra-high-temperature ceramic materials featuring a distinctive array of properties,consisting in high hardness,low thermal conductivity,elevated melting point,superior strength,and exceptional radiation resistance.The outstanding physical properties of high-entropy carbides make them promising candidates for a broad range of applications in extreme service environ-ments,such as advanced turbine engines,nuclear reactors,and hypersonic aircraft.These excellent physical properties arise from such factors as the complex elemental composition,long-range disordered atomic structure,lattice distortions,and enhanced phonon scattering due to vacancy defects.However,the complex candidate compositions and wide variations in the elemental con-tent of high-entropy carbides pose a challenge in rapidly identifying compositional ranges that achieve the desired properties using traditional trial-and-error methods.This article reviews the applications of first-principles calculations and machine learning in the context of high-entropy carbides.It particularly emphasizes the research progress in high-throughput calculations and machine learning for predicting phase stability,mechanical properties,and thermodynamic performance of high-entropy carbides.Finally,the article provides a perspective on the further development of material computations in the realm of ultra-high-temperature ce-ramics based on high-entropy carbides.
关键词
高熵碳化物/第一性原理计算/机器学习/力学性质Key words
high-entropy carbides/first-principle calculations/machine learning/mechanical properties引用本文复制引用
出版年
2024
NETL
NSTL
万方数据