氧化石墨烯/纤维素相互作用的微观要素的机器学习
Machine learning of microscopic ingredients for graphene oxide/cellulose interaction
AI总结：
本研究结合第一性原理计算与机器学习算法，提取控制氧化石墨烯与纤维素结合强度的微观参数，实现了系统的高/低结合能分类及结合能回归预测，为调控该相互作用提供了指导图。
摘要：
理解纳米复合材料中微观属性的作用，能够实现可控的实验系统设计，从而加速该过程。在本研究中，我们通过结合第一性原理计算与机器学习算法，提取了控制氧化石墨烯与纤维素结合强度的相关参数。我们成功将系统划分为两类，即具有较高和较低结合能的系统，这些类别基于孤立的氧化石墨烯特征得到了明确定义。通过理论X射线光电子能谱分析，我们展示了这些相关特征的提取过程。此外，我们通过结合能值与系统特征间的机器学习回归，证明了实现精细控制的可能性。本研究为控制氧化石墨烯/纤维素相互作用提供了指导图。
英文摘要：
Understanding the role of microscopic attributes in nanocomposites allows for a controlled and, therefore, acceleration in experimental system designs. In this work, we extracted the relevant parameters controlling the graphene oxide binding strength to cellulose by combining first-principles calculations and machine learning algorithms. We were able to classify the systems among two classes with higher and lower binding energies, which are well defined based on the isolated graphene oxide features. By a theoretical X-ray photoelectron spectroscopy analysis, we show the extraction of these relevant features. Additionally, we demonstrate the possibilities of a refined control within a machine learning regression between the binding energy values and the system's characteristics. Our work presents a guiding map to the control graphene oxide/cellulose interaction.
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