7 papers
Systematic study of one-point kinetic energy density functionals for atomic nuclei
Tian Shuai Shang, Jian Li, Haozhao Liang +5
To explore the applicability of orbital-free density functional theory (OF-DFT) in nuclear physics, we perform a systematic benchmark of 36 one-point kinetic energy density functio…
Machine learning the two-electron reduced density matrix in molecules and condensed phases
Jessica A. Martinez B., Bhaskar Rana, Xuecheng Shao +2
Machine learning is rapidly accelerating materials and chemical discovery, but most current models target energies, forces, or selected molecular properties rather than the underly…
NEP-CG and NEP-AACG: Efficient coarse-grained and multiscale all-atom-coarse-grained neuroevolution potentials
Zheyong Fan, Wenjun Zhang, Zhenhao Zhang +3
Machine-learned coarse-grained (CG) models often suffer from noisy training data, limiting their accuracy and transferability. We propose a method to generate low-noise training da…
Pseudopotentials for Orbital-Free DFT: Capturing Nonlocality and Correcting Functional Approximants
Valeria Rios-Vargas, Ezekiel Oyeniyi, Xuecheng Shao +4
Developing reliable pseudopotentials for orbital-free density functional theory (OF-DFT), especially for transition metals, remains a significant challenge. In this study, we provi…
Ab initio density functional theory approach to warm dense hydrogen: from density response to electronic correlations
Zhandos A. Moldabekov, Xuecheng Shao, Hannah M. Bellenbaum +5
Understanding the properties of warm dense hydrogen is of key importance for the modeling of compact astrophysical objects and to understand and further optimize inertial confineme…
CrystalFlow: A Flow-Based Generative Model for Crystalline Materials
Xiaoshan Luo, Zhenyu Wang, Qingchang Wang +4
Deep learning-based generative models have emerged as powerful tools for modeling complex data distributions and generating high-fidelity samples, offering a transformative approac…