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| 1 | High-entropy ceramics:Present status,challenges,and a look forward显示文摘High-entropy ceramics (HECs) are solid solutions of inorganic compounds with one or more Wyckoff sites shared by equal or near-equal atomic ratios of multi-principal elements.Although in the infant stage,the emerging of this new family of materials has brought new opportunities for material design and property tailoring.Distinct from metals,the diversity in crystal structure and electronic structure of ceramics provides huge space for properties tuning through band structure engineering and phonon engineering.Aside from strengthening,hardening,and low thermal conductivity that have already been found in high-entropy alloys,new properties like colossal dielectric constant,super ionic conductivity,severe anisotropic thermal expansion coefficient,strong electromagnetic wave absorption,etc.,have been discovered in HECs.As a response to the rapid development in this nascent field,this article gives a comprehensive review on the structure features,theoretical methods for stability and property prediction,processing routes,novel properties,and prospective applications of HECs.The challenges on processing,characterization,and property predictions are also emphasized.Finally,future directions for new material exploration,novel processing,fundamental understanding,in-depth characterization,and database assessments are given. | Huimin XIANG Yan XING Fu-zhi DAI Hongjie WANG Lei SU Lei MIAO Guojun ZHANG Yiguang WANG Xiwei QI Lei YAO Hailong WANG Biao ZHAO Jianqiang LI Yanchun ZHOU | 2021 | Journal of Advanced Ceramics2021，10，3： | 28 |
| 2 | 多元硼化物陶瓷的研究进展显示文摘近年来,由于硼化物具有高强度、高硬度等优良性能,硼化物被广泛应用于耐火材料、核工业、航天航空和切割刀具等领域,硼化物陶瓷成为了世界各国研究的热门和重点。介绍包括二元硼化物、三元硼化物、四元硼化物以及高熵硼化物的陶瓷材料,论述当前多元硼化物陶瓷的研究进展,总结硼化物的结构、性能、制备和应用,概述硼化物陶瓷的发展过程以及多元硼化物在性能方面的优化,并对其在未来的发展与前景进行了展望。 | 龚雨波 赵世鑫 位旭光 宋绍雷 杨治刚 | 2022 | 陶瓷学报2022，43，4： | 3 |
| 3 | Deep potentials for materials science显示文摘To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;i.e.machine learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)method.In this review,we provide an introduction to DP methods in computational materials science.The theory underlying the DP method is presented along with a step-by-step introduction to their development and use.We also review materials applications of DPs in a wide range of materials systems.The DP Library provides a platform for the development of DPs and a database of extant DPs.We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials. | Tongqi Wen Linfeng Zhang Han Wang Weinan E David J Srolovitz | 2022 | Materials Futures2022，1，2： | 2 |
| 4 | 基于机器学习势函数的材料力热性质多尺度模拟研究进展显示文摘随着人工智能技术的发展,采用机器学习方法进行势函数的构建和拟合,成为目前解决经验势函数精度问题的主要技术途径。机器学习方法解决了传统势函数拟合中的试错低效问题,已成为材料设计和物性研究不可或缺的有力工具。本文围绕当前机器学习势函数的特点,及其在相变研究、本征性质研究和界面研究等方面的应用,全面总结介绍势函数及其拟合策略,以及其在特定物性研究中的应用,推动机器学习势函数在材料力热性质的多尺度模拟研究。最后,展望了机器学习势函数所面临的挑战和未来发展前景。 | 吴静 黄安 谢涵鹏 魏东海 李奥南 彭博 王慧敏 秦真真 刘德欢 秦光照 | 2023 | 硅酸盐学报2023，51，2： | 1 |
| 5 | 多尺度模拟计算方法在超高温高熵陶瓷材料中的应用进展显示文摘超高温高熵陶瓷材料以难熔金属碳化物、硼化物、氮化物等为组元,具有较高的硬度、高温强度以及良好的热稳定性,已成为超高温陶瓷领域研究的热点方向之一。与传统材料相比,超高温高熵陶瓷涉及复杂成分空间、多个尺度维度、极端多场耦合服役环境,采用传统经验试错法开发超高温高熵陶瓷效率过低,故而需要改变材料研究范式,依靠多尺度模拟计算方法提高超高温高熵陶瓷研发与应用效率。本文首先简要介绍了具有代表性的多尺度材料计算方法,进而综述了多尺度材料计算方法在超高温高熵陶瓷研究中的典型应用成果,最后对多尺度材料计算方法在超高温高熵陶瓷研究中的前景进行了展望。 | 鲁楠 何鹏飞 种晓宇 胡振峰 梁秀兵 | 2023 | 宇航材料工艺2023，53，1： | 0 |
| 6 | Predicting lattice thermal conductivity via machine learning: a mini review显示文摘Over the past few decades,molecular dynamics simulations and first-principles calculations have become two major approaches to predict the lattice thermal conductivity(κ_(L)),which are however limited by insufficient accuracy and high computational cost,respectively.To overcome such inherent disadvantages,machine learning(ML)has been successfully used to accurately predictκL in a high-throughput style.In this review,we give some introductions of recent ML works on the direct and indirect prediction ofκL,where the derivations and applications of data-driven models are discussed in details.A brief summary of current works and future perspectives are given in the end. | Yufeng Luo Mengke Li Hongmei Yuan Huijun Liu Ying Fang | 2023 | npj Computational Materials2023，，1： | 0 |
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