介电和压电响应的原子分辨机器学习
Atom-Resolved Machine Learning of Dielectric and Piezoelectric Response
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中文总结 AI 辅助
提出DART和LARS框架，以原子索引场学习离子响应，实现小训练集高精度预测介电和压电响应，并在AlN/ScN超晶格中发现高响应候选，性能显著提升。
中文摘要 AI 辅助
预测结构复杂材料中的介电和压电响应需要大型模拟单元，而密度泛函微扰理论（DFPT）计算的复杂度为$\mathcal{O}(N^4)$，在计算上变得不可行。然而，机器学习方法仍然具有挑战性，要么受限于直接回归所需的大量昂贵DFPT数据，要么受限于重建的三次方成本。我们在此提出一个机器学习框架，将离子响应学习为原子索引场：DART（直接原子分辨响应张量学习）直接学习这些场，并从较小的训练集实现高精度，而LARS（线性标度原子分辨响应求解器）通过稀疏线性求解从学习的微观成分重建它们，且不需要用于离子介电或压电张量的DFPT标签。使用DART，我们外推到训练中未包含的314种AlN/ScN超晶格堆叠，并识别出一个高响应极性候选，其DFPT给出的横向夹持压电应变系数$d_{33,f}=14.511$ pC/N和$k_t^2=20.46\\%$，分别超过有序1AlN/1ScN参考值的63%和66%。
英文摘要
Predicting dielectric and piezoelectric responses in structurally complex materials requires large simulation cells, for which density-functional perturbation theory (DFPT) calculations scale as $\mathcal{O}(N^4)$ and become computationally prohibitive. However, machine-learning approaches have remained challenging, limited either by the large amounts of expensive DFPT data required for direct regression or by the cubic cost of reconstruction. We here propose a machine-learning framework that learns the ionic response as an atom-indexed field: DART (Direct Atom-Resolved response-Tensor learning) learns these fields directly and achieves high accuracy from small training sets, whereas LARS (Linear-scaling Atom-Resolved Response Solver) reconstructs them from learned microscopic ingredients through sparse linear solves and requires no DFPT labels for the ionic dielectric or piezoelectric tensors. Using DART, we extrapolate to 314 stackings of AlN/ScN superlattices absent from training and identify a high-response polar candidate, for which DFPT gives a laterally clamped piezoelectric strain coefficient $d_{33,f}=14.511$ pC/N and $k_t^2=20.46\%$, exceeding the ordered 1AlN/1ScN reference by 63% and 66%.
发表机构
- Fudan University（复旦大学）
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