标题： 实时机器学习增强的约束AIMD以在低压和低温下设计从玻璃碳到可淬火非晶金刚石的新路线 显示英文标题

标题： On-the-fly machine learning-augmented constrained AIMD to design new routes from glassy carbon to quenchable amorphous diamond with low pressure and temperature

摘要： 最近机器学习的进展使得具有第一性原理精度的大规模原子模拟成为可能，从而能够精确地建模无序材料，如玻璃碳（GC）。 然而，传统的从头算分子动力学（AIMD）无法有效捕捉各向异性应力效应，而这些效应被认为在极端条件下GC转变为非晶金刚石的过程中起着关键作用。 在这项工作中，我们通过修改VASP 6.3.2，提出了一种实时机器学习增强的约束AIMD（ML-augmented CAIMD）方法。 我们的模拟不仅再现了GC的主要实验特征，还提供了限制性的合成条件和微观见解。 我们表明，GC表现出意想不到的高塑性，其压缩和剪切强度在大应变下得到增强。 在压力下，增加退火温度通过增强sp3保留促进了可淬火非晶金刚石的形成，但在2900 K以上由于热石墨化，这一趋势发生逆转。 在非静水压缩下，GC转变为一种超硬结构，能够承受大的应力差异，当受限压力超过40 GPa时，应力差异急剧增加。 最后，在30 GPa下的严重旋转剪切导致在300至1000 K时sp3分数达到80%。通过在300 K下减压，获得了一种保留64% sp3含量的硬化非晶碳，标志着预测的最低压力-温度路径。 我们的ML增强的CAIMD提供了一个通用框架，用于在各向异性应力下对无序材料的结构转变进行建模。 显示英文摘要

摘要： Recent advances in machine learning have enabled large-scale atomic simulations with first-principles accuracy, allowing precise modeling of disordered materials such as glassy carbon (GC). However, conventional ab initio molecular dynamics (AIMD) cannot effectively capture anisotropic stress effects, which are believed to play a key role in the transformation of GC into amorphous diamond under extreme conditions. In this work, we present an on-the-fly machine learning-augmented constrained AIMD (ML-augmented CAIMD) approach by modifying VASP 6.3.2. Our simulations not only reproduce major experimental features of GC but also provide restrictive synthesis conditions and microscopic insights. We show that GC exhibits unexpectedly high plasticity, with its compressive and shear strengths enhanced by large strains. Under pressure, increasing annealing temperature promotes the formation of quenchable amorphous diamond via enhanced sp3 preservation, but this trend reverses above 2900 K due to thermal graphitization. Under non-hydrostatic compression, GC transforms into a superhard structure sustaining large stress differences, which sharply increase when confining pressure exceeds 40 GPa. Finally, severe rotational shear at 30 GPa induces sp3 fractions up to 80 percent at 300 to 1000 K. A hardened amorphous carbon retaining 64 percent sp3 content is achieved by decompression at 300 K, marking the lowest pressure-temperature route ever predicted. Our ML-augmented CAIMD provides a general framework for modeling structural transformations in disordered materials under anisotropic stresses.