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arXiv 2406.08243cond-mat.mtrl-sci
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探索高熵陶瓷的力学与热学性质:基于通用机器学习势

Exploring mechanical and thermal properties of high-entropy ceramics via general machine learning potentials

Yiwen Liu, Hong Meng, Zijie Zhu, Hulei Yu, Lei Zhuang, Yanhui Chu

AI总结:

本研究利用通用机器学习势(NEP)结合分子动力学模拟,高效探索高熵碳化物的力学与热学性能,验证了其准确性与泛化性,为高熵陶瓷的性能筛选提供了快速方案。

摘要:

高熵陶瓷的力学和热学性能对其在极端条件下的应用至关重要。然而,高熵陶瓷巨大的组分空间严重阻碍了其具有理想力学和热学性能的开发。本文以高熵碳化物(HECs)为模型,展示了基于机器学习势的分子动力学(MD)在探索力学和热学性能方面的效率和有效性。具体而言,我们利用包含等量遍历化学组成的单元和二元碳化物的小型数据集,高效构建了具有广泛组分适用性的通用神经进化势(NEP),该势适用于来自IIIB-VIB族的十种过渡金属元素的高熵碳化物。基于这一完善的NEP,对不同高熵碳化物的力学和热学性能进行的MD模拟与第一性原理计算和实验测量的结果吻合良好,验证了所开发的通用NEP在研究高熵碳化物力学和热学性能方面的准确性、泛化性和可靠性。我们的工作为加速寻找具有理想力学和热学性能的高熵陶瓷提供了一种高效的解决方案。

英文摘要:

The mechanical and thermal performance of high-entropy ceramics are critical to their use in extreme conditions. However, the vast composition space of high-entropy ceramic significantly hinders their development with desired mechanical and thermal properties. Herein, taking high-entropy carbides (HECs) as the model, we show the efficiency and effectiveness of exploring the mechanical and thermal properties via machine-learning-potential-based molecular dynamics (MD). Specifically, a general neuroevolution potential (NEP) with broad compositional applicability for HECs of ten transition metal elements from group IIIB-VIB is efficiently constructed from the small dataset comprising unary and binary carbides with an equal amount of ergodic chemical compositions. Based on this well-established NEP, MD simulations on mechanical and thermal properties of different HECs have shown good agreement with the results of first-principles calculations and experimental measurements, validating the accuracy, generalization, and reliability of using the developed general NEP in investigating mechanical and thermal performance of HECs. Our work provides an efficient solution to accelerate the search for high-entropy ceramics with desirable mechanical and thermal properties.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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