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

<p>Dataset for training the resistive memory-based reservoir graph neural network.</p> <p>In the atomic force calculation experiment,&nbsp;<span lang="EN-HK"><span>a Li</span>₃<span>PO</span>₄<span> dataset is derived from the melting and quenching trajectory via AIMD simulations. The training, validation, and testing datasets consist of 40,000, 5,000, and 5,000 samples, respectively. </span></span></p> <p><span lang="EN-HK"><span>In the Hamiltonian calculation, a dataset </span><span lang="EN-HK">of various graphene (72 atoms) configurations are generated by AIMD simulations at room temperature, with Hamiltonian data calculated via the OpenMX code</span><span lang="EN-HK">.</span><span lang="EN-HK">&nbsp;<span>The training, validation, and testing datasets consist of 270, 90, and 90 samples (including atomic structure and Hamiltonian matrix), respectively.</span></span></span></p> <p>Code:&nbsp; &nbsp;https://github.com/hustmeng/RGNN.git</p> <p>1-Atomic_force_dataset.zip and 2-Hamiltonian_dataset.zip are original data.</p> <p>3-Graph_atomic_force.zip and &nbsp;4-Graph_training_Hamiltonian.zip are graphs.&nbsp;</p> <p>&nbsp;</p> <p>References:</p> <p>&nbsp;</p> <p>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.&nbsp;https://github.com/ken2403/gnnff.git</p> <p>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.&nbsp;https://github.com/mzjb/DeepH-pack.git</p> <p>3. D. Pfau, J.S. Spencer, A.G.D.G. Matthews, W.M.C. Foulkes, Ab initio solution of the many-electron Schr&ouml;dinger equation with deep neural networks, Phys. Rev. Res. 2(3) (2020) 033429.&nbsp;https://github.com/google-deepmind/ferminet.git</p> <p>&nbsp;</p>