Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States.
Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States.
J Chem Theory Comput. 2023 Jul 11;19(13):4182-4201. doi: 10.1021/acs.jctc.2c00827. Epub 2023 Jun 29.
High-throughput electronic structure calculations (often performed using density functional theory (DFT)) play a central role in screening existing and novel materials, sampling potential energy surfaces, and generating data for machine learning applications. By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semilocal DFT and furnish a more accurate description of the underlying electronic structure, albeit at a computational cost that often prohibits such high-throughput applications. To address this challenge, we have constructed a robust, accurate, and computationally efficient framework for high-throughput condensed-phase hybrid DFT and implemented this approach in the PWSCF module of Quantum ESPRESSO (QE). The resulting SeA approach (SeA = SCDM + exx + ACE) combines and seamlessly integrates: () the selected columns of the density matrix method (SCDM, a robust noniterative orbital localization scheme that sidesteps system-dependent optimization protocols), () a recently extended version of exx (a black-box linear-scaling EXX algorithm that exploits sparsity between localized orbitals in real space when evaluating the action of the standard/full-rank operator), and () adaptively compressed exchange (ACE, a low-rank approximation). In doing so, SeA harnesses three levels of computational savings: and from SCDM + exx (which only considers spatially overlapping orbitals on orbital-pair-specific and system-size-independent domains) and from ACE (which reduces the number of calls to SCDM + exx during the self-consistent field (SCF) procedure). Across a diverse set of 200 nonequilibrium (HO) configurations (with densities spanning 0.4-1.7 g/cm), SeA provides a 1-2 order-of-magnitude speedup in the overall time-to-solution, i.e., ≈8-26× compared to the convolution-based PWSCF(ACE) implementation in QE and ≈78-247× compared to the conventional PWSCF(Full) approach, and yields energies, ionic forces, and other properties with high fidelity. As a proof-of-principle high-throughput application, we trained a deep neural network (DNN) potential for ambient liquid water at the hybrid DFT level using SeA via an actively learned data set with ≈8,700 (HO) configurations. Using an out-of-sample set of (HO) configurations (at nonambient conditions), we confirmed the accuracy of this SeA-trained potential and showcased the capabilities of SeA by computing the ground-truth ionic forces in this challenging system containing >1,500 atoms.
高通量电子结构计算（通常使用密度泛函理论（DFT）进行）在筛选现有和新型材料、采样势能面以及为机器学习应用生成数据方面发挥着核心作用。通过包含部分精确交换（EXX），杂化泛函减少了半局域 DFT 中的自相互作用误差，并提供了对基础电子结构更准确的描述，尽管计算成本通常禁止此类高通量应用。为了解决这个挑战，我们构建了一个稳健、准确和高效的高通量凝聚态杂化 DFT 框架，并在 Quantum ESPRESSO（QE）的 PWSCF 模块中实现了这种方法。所得到的 SeA 方法（SeA = SCDM + exx + ACE）结合并无缝集成了：（）密度矩阵方法（SCDM，一种稳健的非迭代轨道定位方案，避免了系统相关的优化协议）的选定列，（）最近扩展的 exx 版本（一种利用在实空间中局部轨道之间的稀疏性来评估标准/全秩 算子作用的黑盒线性标度 EXX 算法），和（）自适应压缩交换（ACE，一种低秩 逼近）。通过这种方式，SeA 利用了三个级别的计算节省：来自 SCDM + exx（仅考虑轨道对特定和系统大小独立域上空间重叠轨道）的 和来自 ACE（在自洽场（SCF）过程中减少 SCDM + exx 的调用次数）的 。在 200 个非平衡（HO）配置（密度范围为 0.4-1.7 g/cm）的多样化集合中，SeA 在总求解时间上提供了 1-2 个数量级的加速，即在 QE 中与基于卷积的 PWSCF(ACE)实现相比，约为 8-26×，与传统的 PWSCF(Full)方法相比，约为 78-247×，并以高精度提供能量、离子力和其他性质。作为一个高通量应用的原理证明，我们使用 SeA 通过一个主动学习的数据集在杂化 DFT 水平上为环境液体水训练了一个深度神经网络（DNN）势，该数据集包含约 8700 个（HO）配置。使用非环境条件下的（HO）配置的外样本集，我们确认了这个 SeA 训练势的准确性，并展示了 SeA 的能力，即在这个包含超过 1500 个原子的具有挑战性的系统中计算出真实的离子力。
