论文标题
大规模有限间隙系统的高通量冷凝相杂交密度理论：海上接近
High-Throughput Condensed-Phase Hybrid Density Functional Theory for Large-Scale Finite-Gap Systems: The SeA Approach
论文作者
论文摘要
高通量DFT计算是筛选现有/新型材料，采样势能表面以及生成用于机器学习的量子机械数据的关键。通过包括精确交换（EXX）的一部分，混合功能减少了半本地DFT中的自我交互误差，并提供了对基础电子结构的更准确描述，尽管通常以高计算成本，但通常会禁止这种高发射应用。为了应对这一挑战，我们已经构建了SEA（SEA = SCDM+EXX+ACE），这是一个在量子浓缩量（QE）的PWSCF模块中高通量凝结相混合DFT的稳健，准确，有效的框架（QE），结合了：（1）由：（1）组合：线性尺度EXX算法（EXX）和（3）自适应压缩交换（ACE）。在一套多样的非平衡（H $ _2 $ O）$ _ {64} $配置（跨度为0.4 g/cm $^3 $^3 $$ -1.7 g/cm $^3 $）中，海上产生一个$ - $ - $ - 两订单的降价速度（〜8x $ 26x）的$ - $ 26x $ - $ 26x，〜8x $ 26x的qudcs in Dounce in Dounce in Stourde in Doune （与常规EXX实施相比，〜78x $ - $ 247倍加速），并产生能量，离子力和其他高保真性能。作为原告证明的高通量应用程序，我们通过〜8,700（H $ _2 $ o）$ _ {64} $配置培训了使用SEA在混合DFT水平上使用SEA在混合DFT水平上进行环境液体水的潜力。我们使用样本外（H $ _2 $ O）$ _ {512} $配置（在非镜子条件下），我们确认了这种海上训练的潜力的准确性，并通过计算该挑战性系统中的> 1,500 Atoms的挑战性系统中的地面离子力来展示海洋能力。
High-throughput DFT calculations are key to screening existing/novel materials, sampling potential energy surfaces, and generating quantum mechanical data for machine learning. By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semi-local DFT and furnish a more accurate description of the underlying electronic structure, albeit at a high computational cost that often prohibits such high-throughput applications. To address this challenge, we have constructed SeA (SeA=SCDM+exx+ACE), a robust, accurate, and efficient framework for high-throughput condensed-phase hybrid DFT in the PWSCF module of Quantum ESPRESSO (QE) by combining: (1) the non-iterative selected columns of the density matrix (SCDM) orbital localization scheme, (2) a black-box and linear-scaling EXX algorithm (exx), and (3) adaptively compressed exchange (ACE). Across a diverse set of non-equilibrium (H$_2$O)$_{64}$ configurations (with densities spanning 0.4 g/cm$^3$$-$1.7 g/cm$^3$), SeA yields a one$-$two order-of-magnitude speedup (~8X$-$26X) in the overall time-to-solution compared to PWSCF(ACE) in QE (~78X$-$247X speedup compared to the conventional EXX implementation) and yields energies, ionic forces, and other properties with high fidelity. As a proof-of-principle high-throughput application, we trained a deep neural network (DNN) potential for ambient liquid water at the hybrid DFT level using SeA via an actively learned data set with ~8,700 (H$_2$O)$_{64}$ configurations. Using an out-of-sample set of (H$_2$O)$_{512}$ configurations (at non-ambient 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.
