The marriage of density functional theory (DFT) and deep-learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach to represent th
theorysystemDensity functional theory(DFT)has been a critical component of computational materials research and discovery for decades.However,the computational cost of solving the central Kohn鈥揝ham equation remains a major obstacle for dynamical studies of complex phenomena at-scale.Here,we propose an...
Orbital-Free Density Functional Theory: An Attractive Electronic Structure Method for Large-Scale First-Principles Simulations Authors: Wenhui Mi, Kai Luo, S. B. Trickey … Predicting electronic structures at any length scale with machine learning Authors: Lenz Fiedler, Normand A. Modine, Steve Schm...
@article{deeph, author = {Li, He and Wang, Zun and Zou, Nianlong and Ye, Meng and Xu, Runzhang and Gong, Xiaoxun and Duan, Wenhui and Xu, Yong}, title = {Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation}, journal = {Nature ...
et al. Identification of potential inhibitors of the E2 protein of Eastern equine encephalitis virus (EEEV) using molecular docking, density functional theory, and molecular dynamics simulations: an in silico approach. Chem. Pap. 79, 3065–3084 (2025). https://doi.org/10.1007/s11696-025-03988...
To make progress, in this work, we systematically study the effects of the elemental doping of Kaolinite on the CO2 adsorption by using the density functional theory (DFT) calculations. Our results show that the elemental doping in the kaolinite has obvious impact on the way of the kaolinite-...
[2] Chen, Y., Zhang, L., Wang, H. and E, W., 2021. DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory. Journal of Chemical Theory and Computation, 17(1), pp.170–181. Contributors5
Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn–Sham DFT, which is increasingly desired for contemporary molecular research. However, its accuracy is limited by the
where high-throughput calculations were done with the assistance of a GAN model and density functional theory (DFT). We studied the most important elemental and electronic properties, which are helpful to distinguish the nonmetals and metals using the machine learning models. In addition, we carried...
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory We propose a general machine learning-based framework for building an accurate and widely-applicable energy functional within the framework of generalized Kohn-Sham density functional theory. To this end, we...