Code for efficient modelling of ionic and electronic interactions by resistive memory- based reservoir graph neural network

Code for reservoir graph neural network in Atomic force, Hamiltonian and wavefunction calculation experiments. Dataset is available at https://doi.org/10.5281/zenodo.13346149 References: 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 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 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.git