机器学习引导探索杂化非本征铁电体Ca3Ti2O7中的畴壁
Machine-learning-guided exploration of domain walls in the hybrid improper ferroelectric Ca3Ti2O7
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中文总结 AI 辅助
本研究利用群论和机器学习原子间势探索Ca3Ti2O7中的复杂畴壁结构，通过电荷感知训练降低形成能误差70%，并揭示低能反极性极化切换路径。
中文摘要 AI 辅助
Ruddlesden-Popper相是高度可调且自然层状的结构，其中极化可通过杂化非本征铁电机制产生。这使得复杂的畴壁（DW）结构成为可能，其中多个序参量，如八面体旋转、极性畸变和应变，相互作用。在本工作中，我们探索了原型Ca3Ti2O7中丰富的畴壁结构，利用群论和机器学习原子间势（MLIPs）绘制了{100}、{110}和{001}伪四方平面中的畴壁。训练得到的势函数重现了密度泛函理论（DFT）对所有考虑的壁类型和取向的序参量和极化分布。结合参考Born有效电荷的电荷感知训练框架将畴壁形成能的预测误差降低了70%，揭示了包含长程静电相互作用以模拟对称性破缺界面的重要性。最后，MLIP被用于以接近DFT计算的精度识别原子尺度的最小能量路径，揭示了极化切换的低能反极性构型。
英文摘要
Ruddlesden-Popper phases are highly tunable and naturally layered structures, in which polarization can arise via a hybrid improper ferroelectric mechanism. This enables a complex domain wall (DW) structure where multiple order parameters, like octahedral rotations, polar distortions and strain, interact. In this work, we explore the rich set of DW structures in prototypical Ca3Ti2O7, mapping out the DWs in the {100}, {110} and {001} pseudo-tetragonal planes using group theory and machine-learned interatomic potentials (MLIPs). The trained potential reproduces the density functional theory (DFT) order parameter and polarization profiles for all wall types and orientations considered. A charge-aware training framework combined with reference Born effective charges reduces the prediction errors for the DW formation energies by 70%, revealing the importance of including long-range electrostatics to model symmetry-broken interfaces. Finally, the MLIP is used to identify minimum energy pathways at the atomic scale with nearly the precision of DFT calculations, revealing a low-energy antipolar configuration for polarization switching.
发表机构
- NTNU Norwegian University of Science and Technology（挪威科技大学）
- Chalmers University of Technology（查尔姆斯理工大学）
- Durham University（杜伦大学）
机构由 AI 辅助整理，请以论文原文为准。
