加速MXenes中的晶格导热计算:一种机器学习力场方法

IF 6.5 Q2 CHEMISTRY, PHYSICAL
Thanasee Thanasarnsurapong, Sourav Kanti Jana, Panyalak Detrattanawichai, Waraporn Namunmong, Wisit Hirunpinyopas, Pawin Iamprasertkun and Adisak Boonchun*, 
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引用次数: 0

摘要

传统上,晶格导热系数是使用声子玻尔兹曼输运方程(PBTE)结合密度泛函理论(DFT)计算来评估的。然而,这种方法是计算密集型的。在这项研究中,我们利用基于主动dft的动态机器学习电场(MLFF)预测了Ti2C和Ti3C2 MXenes及其具有表面末端(O, F, OH)的功能化变体的晶格热导率。Ti2C和Ti3C2的预测热导率分别为73.10和101.15 W m-1 K-1,与之前计算的DFT值接近。表面官能团的引入显著降低了晶格的导热系数。此外,基于mlff的晶格热导率预测比传统的DFT计算快几十到几千倍,极大地加快了MXenes中热输运的研究。这种效率突出了MLFF作为探索和优化二维材料热性能的强大工具的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accelerating Lattice Thermal Conductivity Calculations in MXenes: A Machine Learning Force Field Approach

Traditionally, lattice thermal conductivity is evaluated using the phonon Boltzmann transport equation (PBTE) in combination with density functional theory (DFT) calculations. However, this approach is computationally intensive. In this study, we predicted lattice thermal conductivity of Ti2C and Ti3C2 MXenes, along with their functionalized variants featuring surface terminations (O, F, OH), by using active DFT-based on-the-fly machine learning force fields (MLFF). The predicted thermal conductivities of Ti2C and Ti3C2 are 73.10 and 101.15 W m–1 K–1, respectively, close to previously calculated DFT values. The introduction of surface functional groups significantly reduces the lattice thermal conductivity. Furthermore, the MLFF-based predictions of lattice thermal conductivity are tens to thousands of times faster than conventional DFT calculations, dramatically accelerating the study of thermal transport in MXenes. This efficiency highlights the potential of MLFF as a powerful tool for exploring and optimizing the thermal properties of two-dimensional materials.

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来源期刊
ACS Materials Au
ACS Materials Au 材料科学-
CiteScore
5.00
自引率
0.00%
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0
期刊介绍: ACS Materials Au is an open access journal publishing letters articles reviews and perspectives describing high-quality research at the forefront of fundamental and applied research and at the interface between materials and other disciplines such as chemistry engineering and biology. Papers that showcase multidisciplinary and innovative materials research addressing global challenges are especially welcome. Areas of interest include but are not limited to:Design synthesis characterization and evaluation of forefront and emerging materialsUnderstanding structure property performance relationships and their underlying mechanismsDevelopment of materials for energy environmental biomedical electronic and catalytic applications
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