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通过机器学习探索大化学空间,寻找热力学稳定和机械强大的MXenes

Jaejung Park¹, Minseon Kim², Heekyu Kim¹

¹Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.

Physical chemistry chemical physics : PCCP

|March 22, 2024

概括

机器学习加速了用于先进电子的稳定MXenes的发现. 该框架有效地选材料,识别具有可取的机械性能的新型稳定MXene化合物,用于半导体和晶体管等应用.

科学领域:

材料科学材料科学材料科学
计算材料科学科学计算材料科学
纳米技术纳米技术

背景情况:

作为一类二维材料的MXenes对于热电设备,半导体和晶体管中的应用至关重要.
了解MXene的热力学和机械性能是其有效利用的关键,但传统方法是计算密集的.

研究的目的:

开发基于机器学习 (ML) 的高通量选框架,用于识别热力学稳定的MXenes.
预测和分析稳定的MXenes的机械性能.

主要方法:

利用了23857个MXenes的大数据集用于ML模型培训和验证.
采用密度函数理论 (DFT) 计算来验证ML对热力学稳定性的预测 (形成的热量,凸船体上方的能量).
对验证的稳定MXenes进行了分析的平面内刚度,剪切模块和Poisson比率.

主要成果:

ML框架成功预测了48个稳定的MXenes,其中45个被DFT验证.
确定了23种新的稳定MXenes,包括Ti2CClBr和Zr2NCl2.2.
确定了由构成元素和结构影响的广泛的机械性质 (刚性,模块,波桑比率).
具有基终端的4组过渡金属MXenes表现出卓越的稳定性和机械强度.

结论:

基于机器学习的框架显著加快了稳定和机械强大的MXenes的发现.
元素和结构因子 (每原子体积,键长) 之间的电子阴性差异对于MXene的稳定性和强度至关重要.
这种方法具有巨大的潜力,可以加速开发用于电子应用的基于MXene的新材料.

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