Clary Jacob M, Hull Olivia A, Weinberg Daniel, Sundararaman Ravishankar, Del Ben Mauro, Vigil-Fowler Derek
Materials, Chemical, and Computational Science Directorate, National Renewable Energy Laboratory, Golden, Colorado 80401, United States.
Applied Mathematics & Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.
J Chem Theory Comput. 2025 May 13;21(9):4718-4729. doi: 10.1021/acs.jctc.4c01276. Epub 2025 Apr 14.
Modeling complex materials using high-fidelity, ab initio methods at low cost is a fundamental goal for quantum chemical software packages. The GW approximation and random phase approximation (RPA) provide a unified description of both electronic structure and total energies using the same physics in a many-body perturbative approach that can be more accurate than generalized-gradient density functional theory (DFT) methods. However, GW/RPA implementations have historically been limited to either specific materials classes or application toward small chemical systems. The static subspace approximation allows for reduced cost full-frequency GW/RPA calculations and has previously been benchmarked thoroughly for GW calculations. Here, we describe our approach to including partial occupations of electronic orbitals in full-frequency GW and RPA calculations for the study of electrocatalysts. We benchmarked RPA total energy calculations using the subspace approximation across a diverse test suite of materials for a variety of computational parameters. The benchmarking quantifies the impact of different extrapolation procedures for representing the static polarizability at infinite screened cutoff, and shows that using screened cutoffs above 20-25 Ryd result in diminishing accuracy returns for predicting RPA total energies. Additionally, for moderately sized electrocatalytic models, 2-3 times fewer computational resources are used to compute RPA total energies by representing the static polarizability with 20-30% of the static subspace basis, with an error of approximately 0.01 eV or better in RPA adsorption energy calculations. Finally, we show that for these electrochemical models RPA can shift DFT adsorption energy shifts by up to 0.5 eV and that GW can frequently shift DFT eigenvalues of surface and adsorbate states by approximately 0.5-1 eV.
使用高保真的从头算方法低成本地模拟复杂材料是量子化学软件包的一个基本目标。GW近似和随机相位近似(RPA)在多体微扰方法中使用相同的物理原理,对电子结构和总能量提供了统一的描述,该方法可能比广义梯度密度泛函理论(DFT)方法更准确。然而,GW/RPA的实现历来仅限于特定的材料类别或应用于小型化学系统。静态子空间近似允许降低全频GW/RPA计算的成本,并且此前已针对GW计算进行了全面的基准测试。在这里,我们描述了在用于电催化剂研究的全频GW和RPA计算中纳入电子轨道部分占据的方法。我们使用子空间近似在各种计算参数的不同材料测试套件上对RPA总能量计算进行了基准测试。该基准测试量化了在无限屏蔽截止下表示静态极化率的不同外推程序的影响,并表明使用高于20 - 25里德的屏蔽截止会导致预测RPA总能量的精度回报递减。此外,对于中等规模的电催化模型,通过用20 - 30%的静态子空间基表示静态极化率来计算RPA总能量,所使用的计算资源减少2 - 3倍,在RPA吸附能计算中的误差约为0.01 eV或更小。最后,我们表明对于这些电化学模型,RPA可以使DFT吸附能变化高达0.5 eV,并且GW可以经常使表面和吸附质态的DFT本征值变化约0.5 - 1 eV。