Efficient modelling of ionic and electronic interactions by resistive memory-based reservoir graph neural network

Dataset for the resistive memory-based reservoir graph neural network.References:<br>1. C.W. Park, M. Kornbluth, J. Vandermause, C. Wolverton, B. Kozinsky, J.P. Mailoa, Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture, npj Comput. Mater. 7(1) (2021) 73. https://github.com/ken2403/gnnff.git<br>2. H. Li, Z. Wang, N. Zou, M. Ye, R. Xu, X. Gong, W. Duan, Y. Xu, Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation, Nat. Comput. Sci. 2(6) (2022) 367-377.https://github.com/mzjb/DeepH-pack.git<br>3. D. Pfau, J.S. Spencer, A.G.D.G. Matthews, W.M.C. Foulkes, Ab initio solution of the many-electron Schrödinger equation with deep neural networks, Phys. Rev. Res. 2(3) (2020) 033429.https://github.com/google-deepmind/ferminet.gitAll the code is extended based on the above references, with the primary goal of deploying these models onto hardware systems based on resistive memory chips. Thanks to the original authors for their generous sharing. If you need to use the codes, please refer to the original version of the code and literature.基于阻性存储器（resistive memory）的储层图神经网络（reservoir graph neural network）数据集。 参考文献： 1. C.W. Park、M. Kornbluth、J. Vandermause、C. Wolverton、B. Kozinsky、J.P. Mailoa. 具有直接力架构的高精度可扩展图神经网络力场与分子动力学[J]. npj Computational Materials, 2021, 7(1): 73. 代码仓库：https://github.com/ken2403/gnnff.git 2. H. Li、Z. Wang、N. Zou、M. Ye、R. Xu、X. Gong、W. Duan、Y. Xu. 面向高效从头算电子结构计算的深度学习密度泛函理论哈密顿量[J]. Nature Computational Science, 2022, 2(6): 367-377. 代码仓库：https://github.com/mzjb/DeepH-pack.git 3. D. Pfau、J.S. Spencer、A.G.D.G. Matthews、W.M.C. Foulkes. 基于深度神经网络的多电子薛定谔方程从头算求解[J]. Physical Review Research, 2020, 2(3): 033429. 代码仓库：https://github.com/google-deepmind/ferminet.git 本项目所有代码均基于上述参考文献进行扩展，核心目标为将这些模型部署至基于阻性存储器芯片的硬件系统中。感谢原作者的慷慨分享。若需使用本代码，请参考原代码与文献的原始版本。