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Ehsan Shahini¹, Narendra Chaulagain², Karthik Shankar²

¹Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.

ACS applied materials & interfaces

|November 8, 2023

概括

机器学习模型预测了石墨碳化物 (g-C3N4) 纳米板的脱皮和溶解能量. 这加快了选择最佳溶剂,通过液相脱皮来合成2Dg-C3N4.

科学领域:

材料科学材料科学材料科学
纳米技术纳米技术
计算化学计算化学

背景情况:

石墨碳化物 (g-C3N4) 是一种无金属,对可见光敏感的材料,对于太阳能转换和薄膜晶体管至关重要.
液相脱皮 (LPE) 是合成2D g-C3N4纳米片的关键方法,但溶剂选择至关重要.
精确预测脱皮 (ΔGexf) 和溶解 (ΔGsol) 自由能量对于高效的LPE至关重要,但传统方法是有限的.

研究的目的:

开发精确的机器学习 (ML) 模型,用于预测各种溶剂中的g-C3N4的ΔGexf和ΔGsol.
为了加快对g-C3N4.4液相脱皮的最佳溶剂的识别,g-C3N4.
为了解影响g-C3N4脱皮和溶解的溶剂特性提供见解.

主要方法:

使用分子动力学 (MD) 模拟生成了49种溶剂的 ΔGexf 和 ΔGsol 值的数据库.
在数据集中包括溶剂描述符,如密度,表面张力和介电常数.
对比了6个ML算法,额外的树回归器显示了预测自由能量的最佳性能.

主要成果:

开发了一种高度准确的ML模型,用于预测g-C3N4脱皮和溶解的自由能量.
确定了影响LPE过程的关键溶剂描述符.
实验验证通过分散性测试证实了模型的预测.

结论:

机器学习为MD模拟提供了一个有效的替代方案,用于预测g-C3N4 LPE中的溶剂性能.
开发的ML模型和识别的描述符可以指导可扩展g-C3N4纳米板合成的溶剂选择策略.
这种方法有助于推进g-C3N4在太阳能和电子领域的应用.

关键词:

剥落的自由能量是剥落的自由能量.石墨的碳化物石墨的碳化物液相脱皮脱皮方法液相脱皮脱皮机器学习算法的算法分子动力学模拟的模拟.溶解自由能量是一种自由的能量.

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