用神经网络驱动的分子动力学对ScF3和CaZrF6的状态预测方程
John P Stoppelman1, Angus P Wilkinson1,2, Jesse G McDaniel1
1School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, USA.
The Journal of chemical physics
|August 28, 2023
概括
使用密度函数理论 (DFT) 预测材料属性是有前途的,但与ScF3和CaZrF6等负热膨胀 (NTE) 材料的实验达成定量协议需要进一步完善计算方法.
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:
- 使用密度函数理论 (DFT) 的in silico属性预测是晶体材料的一个不断增长的领域.
- 实现DFT预测和实验结果之间的定量协议仍然是一个挑战.
- 物理效应,如电子相关性,互换空间采样,声子不和和和核量子效应 (NQE) 可以影响预测的准确性.
研究的目的:
- 为ScF3和CaZrF6进行第一原理状态方程 (EOS) 预测,这些材料表现出负热膨胀 (NTE).
- 评估当前计算方法在预测EOS和NTE现象中的准确性.
- 研究核量子效应 (NQE) 在这些材料的EOS和NTE中的作用.
主要方法:
- 对ScF3和CaZrF6的神经网络 (NN) 潜力的开发,在广泛的DFT数据上进行训练.
- 使用NN潜力进行直接分子动力学 (MD) 模拟,在广泛的温度和压力范围内预测状态方程.
- 在MD模拟中通过路径积分方法将NQE纳入.
主要成果:
- 开发的NN潜力使得使用更大的超级电池进行高效的模拟,并包括NQE.
- 预测的状态方程与ScF3和CaZrF6.6的实验数据半量化一致.
- 在ScF3中观察到的压力诱导软化现象在模拟中没有被准确地捕捉到.
- 在低温下,NQE对NTE产生了适度的影响,但在高温下没有显著影响EOS预测.
结论:
- 虽然NN潜力对于预测NTE材料的特性是有价值的,但当前的DFT方法,特别是一般化梯度近似 (GGA),可能不足以与实验量化一致.
- 超出GGA的更高水平的电子相关性可能是准确预测所必需的.
- 需要进一步的方法进步,以充分捕捉复杂的现象,如NTE材料的压力诱导软化.
相关概念视频
Predicting Molecular Geometry
34.5K
VSEPR Theory for Determination of Electron Pair Geometries
34.5K
Equation of State
1.7K
The equation of state is an equation that relates physical quantities, such as pressure, volume, temperature, and the number of moles, of a thermodynamics system with each other. The equation relating physical quantities with each other can be a simple mathematical expression or too complicated to express in mathematical form. In either case, a relationship between physical quantities exists. If the equation of state cannot be expressed in a mathematical form, then experimental data and...
1.7K
Real Gases: Effects of Intermolecular Forces and Molecular Volume Deriving Van der Waals Equation
34.7K
Thus far, the ideal gas law, PV = nRT, has been applied to a variety of different types of problems, ranging from reaction stoichiometry and empirical and molecular formula problems to determining the density and molar mass of a gas. However, the behavior of a gas is often non-ideal, meaning that the observed relationships between its pressure, volume, and temperature are not accurately described by the gas laws.
34.7K
Network Covalent Solids
13.5K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
13.5K
Van der Waals Equation
4.2K
The ideal gas law is an approximation that works well at high temperatures and low pressures. The van der Waals equation of state (named after the Dutch physicist Johannes van der Waals, 1837−1923) improves it by considering two factors.
First, the attractive forces between molecules, which are stronger at higher densities and reduce the pressure, are considered by adding to the pressure a term equal to the square of the molar density multiplied by a positive coefficient a. Second, the volume...
First, the attractive forces between molecules, which are stronger at higher densities and reduce the pressure, are considered by adding to the pressure a term equal to the square of the molar density multiplied by a positive coefficient a. Second, the volume...
4.2K
Molecular Models
38.6K
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.
38.6K


