通过拟线性缩放的粒子网格电荷平衡加速第四代机器学习势
Accelerating fourth-generation machine learning potentials by quasi-linear scaling particle mesh charge equilibration
AI总结：
针对第四代机器学习势中电荷平衡计算昂贵的问题，提出无需显式 Coulomb 矩阵元、拟线性缩放的粒子网格 Qeq 方法，并支持含电荷全局结构依赖的高效能量导数计算。
摘要：
机器学习势（MLP）已经变革了原子模拟领域：它以电子结构方法的精度描述原子相互作用，而成本仅为其一小部分。当前大多数 MLP 将系统能量构造为原子能量之和，这些原子能量依赖于以预定义或可学习特征向量形式提供的原子环境信息。此外，若长程电荷转移等非局部现象十分重要，就需要使用第四代 MLP；这类 MLP 包含电荷平衡（Qeq）步骤，以考虑系统的全局结构。该 Qeq 会显著提高计算成本，因此可能成为大体系的计算瓶颈。本文提出一种高效的 Qeq 表述，无需显式计算 Coulomb 矩阵元，从而形成一种拟线性缩放的方法。此外，我们的方法还支持高效计算能量导数，其中显式考虑了由 Qeq 得到的原子电荷对全局结构的依赖性。由于其通用性，该方法并不局限于 MLP，还可应用于多种其他力场。
英文摘要：
Machine learning potentials (MLP) have revolutionized the field of atomistic simulations by describing the atomic interactions with the accuracy of electronic structure methods at a small fraction of the costs. Most current MLPs construct the energy of a system as a sum of atomic energies, which depend on information about the atomic environments provided in form of predefined or learnable feature vectors. If, in addition, non-local phenomena like long-range charge transfer are important, fourth-generation MLPs need to be used, which include a charge equilibration (Qeq) step to take the global structure of the system into account. This Qeq can significantly increase the computational cost and thus can become the computational bottleneck for large systems. In this paper we present a highly efficient formulation of Qeq that does not require the explicit computation of the Coulomb matrix elements resulting in a quasi-linearly scaling method. Moreover, our approach also allows for the efficient calculation of energy derivatives, which explicitly consider the global structure-dependence of the atomic charges as obtained from Qeq. Due to its generality, the method is not restricted to MLPs but can also be applied within a variety of other force fields.
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