Liu Han, Li Yipeng, Fu Zipeng, Li Kevin, Bauchy Mathieu
Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, California 90095, USA.
J Chem Phys. 2020 Feb 7;152(5):051101. doi: 10.1063/1.5136041.
Interatomic forcefields for silicate glasses often rely on partial (rather than formal) charges to describe the Coulombic interactions between ions. Such forcefields can be classified as "soft" or "hard" based on the value of the partial charge attributed to Si atoms, wherein softer forcefields rely on smaller partial charges. Here, we use machine learning to efficiently explore the "landscape" of Buckingham forcefields for silica, that is, the evolution of the overall forcefield accuracy as a function of the forcefield parameters. Interestingly, we find that soft and hard forcefields correspond to two distinct, yet competitive local minima in this landscape. By analyzing the structure of the silica configurations predicted by soft and hard forcefields, we show that although soft and hard potentials offer competitive accuracy in describing the short-range order structure, soft potentials feature a higher ability to describe the medium-range order.
用于硅酸盐玻璃的原子间力场通常依靠部分（而非形式上的）电荷来描述离子之间的库仑相互作用。基于赋予硅原子的部分电荷值，此类力场可分为“软”或“硬”，其中较软的力场依靠较小的部分电荷。在此，我们利用机器学习来高效探索二氧化硅的Buckingham力场“景观”，即整体力场精度随力场参数的变化情况。有趣的是，我们发现在此“景观”中，软力场和硬力场对应于两个不同但相互竞争的局部最小值。通过分析软力场和硬力场预测的二氧化硅构型结构，我们表明，尽管软势和硬势在描述短程有序结构方面具有相当的精度，但软势在描述中程有序方面具有更高的能力。
