论文标题
通过神经网络ANSATZ从头开始计算真实固体
Ab initio calculation of real solids via neural network ansatz
论文作者
论文摘要
近年来，神经网络已应用于针对小分子和物理模型的多体电子相关性。在这里，我们提出了一种新的体系结构，该结构扩展了分子神经网络，其中包含周期性边界条件，以从头开始计算实际固体。在四种不同类型的系统中，即一维周期氢链，二维石墨烯，三维氢化物晶体和均匀的电子气，在其中获得的结果，例如，获得的结果，例如，获得的结果，例如，获得的结果，例如，获得的结果，例如总能量，解离曲线和凝聚力的能量，优于许多传统的从头开始方法，并达到最准确的方法的水平。此外，还计算了典型系统的电子密度，以提供各种固体的物理直觉。我们将分子神经网络扩展到周期系统的方法可以很容易地集成到其他神经网络结构中，从而强调了使用神经网络ANSATZ对更复杂的固体系统的实质未来，并且更普遍地认可机器学习在材料模拟和冷凝物物理学中的应用。
Neural networks have been applied to tackle many-body electron correlations for small molecules and physical models in recent years. Here we propose a new architecture that extends molecular neural networks with the inclusion of periodic boundary conditions to enable ab initio calculation of real solids. The accuracy of our approach is demonstrated in four different types of systems, namely the one-dimensional periodic hydrogen chain, the two-dimensional graphene, the three-dimensional lithium hydride crystal, and the homogeneous electron gas, where the obtained results, e.g. total energies, dissociation curves, and cohesive energies, outperform many traditional ab initio methods and reach the level of the most accurate approaches. Moreover, electron densities of typical systems are also calculated to provide physical intuition of various solids. Our method of extending a molecular neural network to periodic systems can be easily integrated into other neural network structures, highlighting a promising future of ab initio solution of more complex solid systems using neural network ansatz, and more generally endorsing the application of machine learning in materials simulation and condensed matter physics.
