用机器学习力场逼近耦合簇精度，提升碳和氢化锂晶格动力学预测
Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy
- 基于DFT与耦合簇数据训练机器学习力场，引入差分学习与电荷感知机制
- 光学模振动频率比DFT更高，更接近实验值，且能估算氢化锂的非简谐效应
- 适合需要高精度晶格动力学模拟的材料科学家，尤其关注非简谐效应
我们研究了在近似密度泛函理论（DFT）和耦合簇（CC）水平势能面训练的机器学习力场（MLFFs），应用于碳金刚石和氢化锂固体的晶格动力学。通过计算声子色散和振动态密度（VDOS）并与实验及参考从头算结果对比，评估其准确性和精度。为克服长程效应和耦合簇训练数据缺乏原子力的局限，采用基于CC与DFT差异的差分学习方法，以及电荷感知的MLFF方法。相比DFT，基于CC训练的MLFF给出更高的光学模式振动频率，更符合实验结果。此外，该方法用于在耦合簇水平估算氢化锂的非简谐效应对VDOS的影响。
原文摘要 · Abstract (English)
We investigate Machine-Learned Force Fields (MLFFs) trained on approximate Density Functional Theory (DFT) and Coupled Cluster (CC) level potential energy surfaces for the carbon diamond and lithium hydride solids. We assess the accuracy and precision of the MLFFs by calculating phonon dispersions and vibrational densities of states (VDOS) that are compared to experiment and reference ab initio results. To overcome limitations from long-range effects and the lack of atomic forces in the CC training data, a delta-learning approach based on the difference between CC and DFT results, as well as a charge aware MLFF approach is explored. Compared to DFT, MLFFs trained on CC theory yield higher vibrational frequencies for optical modes, agreeing better with experiment. Furthermore, the MLFFs are used to estimate anharmonic effects on the VDOS of lithium hydride at the level of CC theory.
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