Liu Chang, Aguirre Néstor F, Cawkwell Marc J, Batista Enrique R, Yang Ping
Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
J Chem Theory Comput. 2024 Jul 23;20(14):5923-5936. doi: 10.1021/acs.jctc.4c00145. Epub 2024 Jul 11.
Density functional tight binding (DFTB) models for -element species are challenging to parametrize owing to the large number of adjustable parameters. The explicit optimization of the terms entering the semiempirical DFTB Hamiltonian related to orbitals is crucial to generating a reliable parametrization for -block elements, because they play import roles in bonding interactions. However, since the number of parameters grows quadratically with the number of orbitals, the computational cost for parameter optimization is much more expensive for the -elements than for the main group elements. In this work we present a set of efficient approaches for mitigating the hurdle imposed by the large size of the parameter space. A novel group-by-orbital correction functions for two-center bond integrals was developed. With this approach the number of parameters is reduced, and it grows linearly with the number of elements, maintaining the accuracy and the number of parameters, in the case of f elements, by more than 40%. The parameter optimization step was accelerated by means of the mini-batch BFGS method. This method allows parameter optimizations with much larger training sets than other single batch methods. A stochastic optimizer was employed that helped overcome shallow local minima in the objective function. The proposed algorithm was used to parametrize the DFTB Hamiltonian for the Th-O system, which was subsequently applied to the study of ThO nanoparticles. The training set consisted of 6322 unique structures, which is barely feasible with conventional optimization methods. The optimized parameter set, , displays good agreement with DFT-calculated properties such as energies, forces, and structures for both clusters and bulk ThO. Benefiting from the fewer number of parameters and lower computational costs for objective function evaluations, this new approach shows its potential applications in DFTB parametrization for elements with high angular momentum, which present a challenge to conventional methods.
由于存在大量可调参数,对 - 元素物种的密度泛函紧束缚(DFTB)模型进行参数化具有挑战性。明确优化进入与 轨道相关的半经验DFTB哈密顿量的项对于为 - 族元素生成可靠的参数化至关重要,因为它们在键合相互作用中起着重要作用。然而,由于参数数量随轨道数量呈二次方增长, - 元素的参数优化计算成本比主族元素要高得多。在这项工作中,我们提出了一套有效的方法来减轻参数空间大尺寸带来的障碍。开发了一种用于双中心键积分的新颖的按轨道分组校正函数。通过这种方法,参数数量减少,并且随着元素数量线性增长,在f元素的情况下,保持了准确性且参数数量增加了40%以上。通过小批量BFGS方法加速了参数优化步骤。该方法允许使用比其他单批方法大得多的训练集进行参数优化。采用了一种随机优化器,有助于克服目标函数中的浅局部最小值。所提出的算法用于对Th - O系统的DFTB哈密顿量进行参数化,随后将其应用于ThO纳米颗粒的研究。训练集由6322个独特结构组成,这对于传统优化方法来说几乎是不可行的。优化后的参数集 与DFT计算的性质(如簇和块状ThO的能量、力和结构)显示出良好的一致性。受益于较少的参数数量和较低的目标函数评估计算成本,这种新方法在对具有高角动量的元素进行DFTB参数化方面显示出其潜在应用,而这对传统方法来说是一个挑战。