An Efficient Deep Learning Scheme To Predict the Electronic Structure of Materials and Molecules: The Example of Graphene-Derived Allotropes
An Efficient Deep Learning Scheme To Predict the Electronic Structure of Materials and Molecules: The Example of Graphene-Derived Allotropes
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DOI：
10.1021/acs.jpca.0c07458
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发表时间：
2020-11-12
影响因子：
2.9
通讯作者：
Ramprasad, Rampi
中科院分区：
文献类型：
--
作者：
del Rio, Beatriz G.;Kuenneth, Christopher;Ramprasad, Rampi
基于密度泛函理论 (DFT) 的计算正在改变材料研究和发现的各个方面。然而，求解 DFT 的中心方程（即 Kohn-Sham 方程）所需的努力仍然是利用常规计算资源在实际时间内研究具有数百个原子的大型系统的主要障碍。在这里，我们提出了一种深度学习架构，可以系统地学习 Kohn-Sham 方程的输入输出行为，并以前所未有的速度和化学精度预测电子态密度（DFT 计算的主要输出）。当该算法接触到新的不同原子配置时，它还会适应并逐步提高预测能力和多功能性。我们展示了跨越大构型和相空间的多种碳同素异形体的这种能力。电子态密度以及电子电荷密度可在下游用于预测各种材料特性，绕过 Kohn-Sham 方程，从而形成超快和高保真 DFT 仿真器。
Computations based on density functional theory (DFT) are transforming various aspects of materials research and discovery. However, the effort required to solve the central equation of DFT, namely the Kohn-Sham equation, which remains a major obstacle for studying large systems with hundreds of atoms in a practical amount of time with routine computational resources. Here, we propose a deep learning architecture that systematically learns the input-output behavior of the Kohn-Sham equation and predicts the electronic density of states, a primary output of DFT calculations, with unprecedented speed and chemical accuracy. The algorithm also adapts and progressively improves in predictive power and versatility as it is exposed to new diverse atomic configurations. We demonstrate this capability for a diverse set of carbon allotropes spanning a large configurational and phase space. The electronic density of states, along with the electronic charge density, may be used downstream to predict a variety of materials properties, bypassing the Kohn-Sham equation, leading to an ultrafast and high-fidelity DFT emulator.