# Ab initio calculation of real solids via neural network ansatz

@inproceedings{Li2022AbIC, title={Ab initio calculation of real solids via neural network ansatz}, author={Xiang Li and Zhe Li and Ji Chen}, year={2022} }

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…

## 2 Citations

Sampling-free Inference for Ab-Initio Potential Energy Surface Networks

- Computer ScienceArXiv
- 2022

This work proposes the PlaNet framework to simultaneously train a surrogate model that avoids expensive Monte-Carlo integration and reduces inference time from minutes or even hours to milliseconds, which can accurately model high-resolution multi-dimensional energy surfaces that previously would have been unobtainable via neural wave functions.

Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?

- Computer ScienceArXiv
- 2022

A novel deep learning architecture is introduced that achieves 40-70% lower energy error at 8x lower computational cost compared to previous approaches and establishes a new benchmark by calculating the most accurate variational ground state energies ever published for a number of different atoms and molecules.

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