Exploring mechanical and thermal properties of high-entropy ceramics via general machine learning potentials
Abstract
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.
AI-Generated Overview
Here's a brief overview of the text extracted from the scientific paper with one bullet point for each specified category:
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Research Focus: The study investigates the mechanical and thermal properties of high-entropy ceramics, with a particular emphasis on high-entropy carbides (HECs) and their potential for use in extreme conditions.
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Methodology: A general neuroevolution potential (NEP) is constructed using machine-learning techniques based on a small dataset of unary and binary carbides, along with molecular dynamics (MD) simulations to explore the properties of various HECs.
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Results: The NEP demonstrated high accuracy and transferability, allowing for accurate predictions of mechanical and thermal properties of HECs, which aligned well with first-principles calculations and experimental measurements.
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Key Contribution(s): This work establishes an efficient machine-learning framework to accelerate the exploration and discovery of high-entropy ceramics with desirable mechanical and thermal properties.
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Significance: The findings provide a simple and effective approach to developing high-entropy ceramics that could enhance materials design for applications requiring superior performance under extreme conditions.
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Broader Applications: The method and findings have implications for various structural and functional applications in materials science, particularly for high-performance ceramics in fields such as aerospace, cutting tools, and thermal protection systems.