一个机器学习框架用于建模原子失序材料的整体性质
Zhenyao Fang1, Ting-Wei Hsu1, Qimin Yan1
1Department of Physics, Northeastern University, Boston, Massachusetts 02115, United States.
ACS nano
|October 13, 2025
概括
使用图形神经网络 (GNN) 的机器学习模型现在可以有效地模拟材料中的原子混乱. 这种方法揭示了乱如何影响MXenes中的电导率,而不是光导率.
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
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 原子的混乱显著影响物质的特性,如电荷传输和催化.
- 由于计算成本,第一原则方法难以建模乱效应.
- 机器学习,特别是图形神经网络 (GNN),为复杂的材料属性预测提供了高效的解决方案.
研究的目的:
- 开发一种机器学习辅助的框架,用于模拟无序材料的热力学和整体平均属性.
- 为了研究原子混乱对MXene单层的功能性质的影响.
主要方法:
- 集成等价图形神经网络 (GNN) 与蒙特卡洛模拟.
- 对无序材料的一般计算框架的开发.
- 使用表面终结失序的MXene (Ti3C2T2-x) 作为模型系统.
主要成果:
- 在Ti3C2T2-x中,由于电子散射和兴奋剂,电导率在秩序-混乱过渡附近呈现峰值.
- 光导率对局部原子失调不敏感,反映了全球表面的组成.
- 证明了框架在统计学上模拟疾病影响的能力.
结论:
- 原子的混乱在确定材料属性的过程中起着至关重要的作用.
- 开发的机器学习框架为研究高合金和自旋液体等无序系统提供了强大的工具.
- 区分无序对不同材料性质的局部和全球影响.
更多相关视频
相关概念视频
Molecular Models
43.5K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
43.5K
Predicting Molecular Geometry
45.0K
VSEPR Theory for Determination of Electron Pair Geometries
45.0K
Intrinsically Disordered Proteins
19.2K
Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...
19.2K
Intrinsically Disordered Proteins
2.8K
2.8K
Atomic Orbitals
43.0K
An atomic orbital represents the three-dimensional regions in an atom where an electron has the highest probability to reside. The radial distribution function indicates the total probability of finding an electron within the thin shell at a distance r from the nucleus. The atomic orbitals have distinct shapes which are determined by l, the angular momentum quantum number. The orbitals are often drawn with a boundary surface, enclosing densest regions of the cloud.
43.0K
Atomic Force Microscopy
4.4K
Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...
4.4K


