Buccheri Alexander, Li Rui, Deustua J Emiliano, Moosavi S Mohamad, Bygrave Peter J, Manby Frederick R
School of Chemistry, University of Bristol, Cantocks Close, Bristol BS8 1TS, United Kingdom.
Department of Physics, Max Planck Institute for the Structure and Dynamics of Matter, Luruper Ch 149, 22761 Hamburg, Germany.
J Chem Theory Comput. 2025 Feb 25;21(4):1615-1625. doi: 10.1021/acs.jctc.4c01234. Epub 2025 Feb 5.
A novel formulation is presented for the treatment of electrostatics in the periodic GFN1-xTB tight-binding model. Periodic GFN1-xTB is hindered by the functional form of the second-order electrostatics, which only recovers Coulombic behavior at large interatomic distances and lacks a closed-form solution for its Fourier transform. We address this by introducing a binomial expansion of the Klopman-Ohno function to partition short- and long-range interactions, enabling the use of a generalized Ewald summation for the solution of the electrostatic energy. This approach is general and is applicable to any damped potential of the form | + |. Benchmarks on the X23 molecular crystal dataset and a range of prototypical bulk semiconductors demonstrate that this systematic treatment of the electrostatics eliminates unphysical behavior in the equation of state curves. In the bulk systems studied, we observe a mean absolute error in total energy of 35 meV/atom, comparable to the machine-learned universal force field, M3GNet, and sufficiently precise for structure relaxation. These results highlight the promising potential of GFN1-xTB as a universal tight-binding parametrization.
本文提出了一种用于处理周期性GFN1-xTB紧束缚模型中静电问题的新公式。周期性GFN1-xTB受到二阶静电函数形式的阻碍，该函数仅在大原子间距时恢复库仑行为，并且其傅里叶变换缺乏封闭形式的解。我们通过引入克洛普曼-大野函数的二项式展开来划分短程和长程相互作用，从而能够使用广义埃瓦尔德求和来求解静电能。这种方法具有通用性，适用于任何形式为| + |的阻尼势。对X23分子晶体数据集和一系列典型体半导体的基准测试表明，这种对静电的系统处理消除了状态方程曲线中的非物理行为。在所研究的体系统中，我们观察到总能量的平均绝对误差为35 meV/原子，与机器学习的通用力场M3GNet相当，并且对于结构弛豫来说足够精确。这些结果突出了GFN1-xTB作为一种通用紧束缚参数化方法的潜在前景。
