论文标题
电子结构的神经网络表示，$ ab $ $ $ $ $ intio $分子动态
Neural network representation of electronic structure from $ab$ $initio$ molecular dynamics
论文作者
论文摘要
尽管具有丰富的信息内容，但电子结构数据以$ ab $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $分子动力学模拟的高量收集的电子结构数据通常被未能充分利用。我们以紧密结合的汉密尔顿材料的形式引入了此类数据的可转移的高保真神经网络表示形式。 $ ab $ $ $ initio $电子结构的这种预测性表示，结合机器学习的增强分子动力学，可以有效，准确的电子演化和采样。当应用于一维电荷密度波材料Carbyne时，我们能够计算规范集合中的光谱函数和光学电导率。在孤子 - 抗溶剂对歼灭过程中评估的光谱函数揭示了由于延迟的电子静态耦合而超出了born-oppenheimer限制，因此低能量边缘模式的重新归一化。电子结构动力学系统的有效且可重复使用的替代模型的可用性将使计算许多有趣的物理特性，铺平方法，以在材料建模中无法访问或具有挑战性的途径。
Despite their rich information content, electronic structure data amassed at high volumes in $ab$ $initio$ molecular dynamics simulations are generally under-utilized. We introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials. This predictive representation of $ab$ $initio$ electronic structure, combined with machine-learning boosted molecular dynamics, enables efficient and accurate electronic evolution and sampling. When applied to a one-dimension charge-density wave material, carbyne, we are able to compute the spectral function and optical conductivity in the canonical ensemble. The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born-Oppenheimer limit. The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable calculating many interesting physical properties, paving way to previously inaccessible or challenging avenues in materials modeling.
