标题： 等变机器学习电场梯度——预测MAPbI$_3$相变中的四极耦合常数 显示英文标题

标题： Equivariant machine learning of Electric Field Gradients -- Predicting the quadrupolar coupling constant in the MAPbI$_3$ phase transition

摘要： 我们提出了一种结合机器学习和第一性原理计算的策略，以实现高精度的核四极耦合常数预测。 我们的方法采用了两种不同的机器学习框架：一种是机器学习力场，用于生成分子动力学轨迹，另一种是保留旋转和平移对称性的电场梯度模型。 通过引入热浴驱动的分子动力学采样，我们能够在有限温度下预测高度无序材料的四极耦合常数。 我们通过预测有机-无机卤化物钙钛矿 MAPbI$_3$的四四方相到立方相转变温度来验证我们的方法，得到的结果与实验数据非常接近。 显示英文摘要

摘要： We present a strategy combining machine learning and first-principles calculations to achieve highly accurate nuclear quadrupolar coupling constant predictions. Our approach employs two distinct machine-learning frameworks: a machine-learned force field to generate molecular dynamics trajectories and a second model for electric field gradients that preserves rotational and translational symmetries. By incorporating thermostat-driven molecular dynamics sampling, we enable the prediction of quadrupolar coupling constants in highly disordered materials at finite temperatures. We validate our method by predicting the tetragonal-to-cubic phase transition temperature of the organic-inorganic halide perovskite MAPbI$_3$, obtaining results that closely match experimental data.