An Efficient Deep Learning Scheme To Predict the Electronic Structure of Materials and Molecules: The Example of Graphene-Derived Allotropes,The Journal of Physical Chemistry A

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An Efficient Deep Learning Scheme To Predict the Electronic Structure of Materials and Molecules: The Example of Graphene-Derived Allotropes
The Journal of Physical Chemistry A ( IF 2.7 ) Pub Date : 2020-11-03 , DOI: 10.1021/acs.jpca.0c07458
Beatriz G. del Rio ₁ , Christopher Kuenneth ₁ , Huan Doan Tran ₁ , Rampi Ramprasad ₁

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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.

中文翻译：

预测材料和分子电子结构的有效深度学习方案：以石墨烯为原料的同素异形体为例

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。但是，解决DFT中心方程即Kohn-Sham方程所需的工作量仍然是使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体系结构，该体系结构可以系统地学习Kohn-Sham方程的输入输出行为，并以前所未有的速度和化学准确性预测DFT计算的主要输出状态电子密度。该算法在暴露于新的不同原子构型时，还适应并逐步提高了预测能力和多功能性。我们证明了这种能力可用于跨越较大构型和相空间的多种碳同素异形体。状态的电子密度以及电荷密度可以在下游用于预测各种材料的性能，从而绕过Kohn-Sham方程，从而实现了超快和高保真的DFT仿真器。

更新日期：2020-11-12

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