Reicht Lukas, Legenstein Lukas, Wieser Sandro, Zojer Egbert
Institute of Solid State Physics, NAWI Graz, Graz University of Technology, 8010 Graz, Austria.
Institute of Materials Chemistry, TU Wien, 1060 Vienna, Austria.
Molecules. 2024 Aug 6;29(16):3724. doi: 10.3390/molecules29163724.
The phonon-related properties of crystalline polymers are highly relevant for various applications. Their simulation is, however, particularly challenging, as the systems that need to be modeled are often too extended to be treated by ab initio methods, while classical force fields are too inaccurate. Machine-learned potentials parametrized against material-specific ab initio data hold the promise of being extremely accurate and also highly efficient. Still, for their successful application, protocols for their parametrization need to be established to ensure an optimal performance, and the resulting potentials need to be thoroughly benchmarked. These tasks are tackled in the current manuscript, where we devise a protocol for parametrizing moment tensor potentials (MTPs) to describe the structural properties, phonon band structures, elastic constants, and forces in molecular dynamics simulations for three prototypical crystalline polymers: polyethylene (PE), polythiophene (PT), and poly-3-hexylthiophene (P3HT). For PE, the thermal conductivity and thermal expansion are also simulated and compared to experiments. A central element of the approach is to choose training data in view of the considered use case of the MTPs. This not only yields a massive speedup for complex calculations while essentially maintaining DFT accuracy, but also enables the reliable simulation of properties that, so far, have been entirely out of reach.
结晶聚合物的声子相关性质与各种应用高度相关。然而,对其进行模拟极具挑战性,因为需要建模的系统通常过于庞大,无法用从头算方法处理,而经典力场又不够精确。针对特定材料的从头算数据进行参数化的机器学习势有望极其精确且高效。尽管如此,为了成功应用它们,需要建立其参数化协议以确保最佳性能,并且需要对所得势进行全面的基准测试。当前手稿解决了这些任务,我们设计了一种用于对矩张量势(MTP)进行参数化的协议,以描述三种典型结晶聚合物:聚乙烯(PE)、聚噻吩(PT)和聚3-己基噻吩(P3HT)在分子动力学模拟中的结构性质、声子能带结构、弹性常数和力。对于PE,还模拟了热导率和热膨胀并与实验进行了比较。该方法的一个核心要素是根据MTP的考虑使用情况选择训练数据。这不仅在基本保持密度泛函理论(DFT)精度的同时大幅加速了复杂计算,还能够可靠地模拟迄今为止完全无法实现的性质。