140 citations · 404 across the 21 of their papers we have counts for
24 papers
Accurately Generalizing Deep-Learning Hamiltonian Methods to Plane-Wave Density Functional Theory
Chuntian Wu, Jiaxiang Li, Qiang Xu +4
Deep neural networks that predict density functional theory (DFT) Hamiltonians from material structures offer a route to efficient electronic-structure calculations. However, exten…
ARES: a plane-wave density-functional-theory code in Rust
Zehan Liu, Qian Wang, Xiangyu Zheng +7
ARES (Ab initio atomic mateRial modEling Software at JLU) is a plane-wave density-functional-theory (DFT) code written entirely in Rust as a layered 22-crate workspace. It supports…
The quadratic density response function for non-interacting fermions at arbitrary temperature
Pontus Svensson, Jan Vorberger, Zhandos A. Moldabekov +5
We develop and implement the quadratic density response function of non-interacting fermions at arbitrary temperature, frequencies, and wave vectors. Starting from a Green's functi…
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…
Density-Functionalized QM/MM Delivers Chemical Accuracy For Solvated Systems
Xin Chen, Jessica Martinez, Xuecheng Shao +4
We present a reformulation of QM/MM as a fully quantum mechanical theory of interacting subsystems, all treated at the level of density functional theory (DFT). For the MM subsyste…
Nonlocal vs Local Pseudopotentials Affect Kinetic Energy Kernels in Orbital-Free DFT
Zhandos A. Moldabekov, Xuecheng Shao, Michele Pavanello +2
The kinetic energy (KE) kernel, which is defined as the second order functional derivative of the KE functional with respect to density, is the key ingredient to the construction o…
Accelerating Structural Optimization through Fingerprinting Space Integration on the Potential Energy Surface
Shuo Tao, Xuecheng Shao, Li Zhu
Structural optimization has been a crucial component in computational materials research, and structure predictions have relied heavily on this technique in particular. In this stu…
Bound state breaking and the importance of thermal exchange-correlation effects in warm dense hydrogen
Zhandos Moldabekov, Sebastian Schwalbe, Maximilian Böhme +5
Hydrogen at extreme temperatures and pressures is ubiquitous throughout our universe and naturally occurs in a variety of astrophysical objects. In addition, it is of key relevance…
Imposing Correct Jellium Response Is Key to Predict the Density Response by Orbital-Free DFT
Zhandos A. Moldabekov, Xuecheng Shao, Michele Pavanello +3
Orbital-free density functional theory (OF-DFT) constitutes a computationally highly effective tool for modeling electronic structures of systems ranging from room-temperature mate…
Machine Learning Electronic Structure Methods Based On The One-Electron Reduced Density Matrix
Xuecheng Shao, Lukas Paetow, Mark E. Tuckerman +1
The theorems of density functional theory (DFT) and reduced density matrix functional theory (RDMFT) establish a bijective map between the external potential of a many-body system…
Accelerating Equilibration in First-Principles Molecular Dynamics with Orbital-Free Density Functional Theory
Lenz Fiedler, Zhandos A. Moldabekov, Xuecheng Shao +4
We introduce a practical hybrid approach that combines orbital-free density functional theory (DFT) with Kohn-Sham DFT for speeding up first-principles molecular dynamics simulatio…
Nonlocal and nonadiabatic Pauli potential for time-dependent orbital-free density functional theory
Kaili Jiang, Xuecheng Shao, Michele Pavanello
Time-dependent orbital-free density functional theory (TD-OFDFT) is an efficient ab-initio method for calculating the electronic dynamics of large systems. In comparison to standar…
A Symmetry-orientated Divide-and-Conquer Method for Crystal Structure Prediction
Xuecheng Shao, Jian Lv, Peng Liu +5
Crystal structure prediction has been a subject of topical interest, but remains a substantial challenge, especially for complex structures as it deals with the global minimization…
GGA-Level Subsystem DFT Achieves Sub-kcal/mol Accuracy Intermolecular Interactions by Mimicking Nonlocal Functionals
Xuecheng Shao, Wenhui Mi, Michele Pavanello
The key feature of nonlocal kinetic energy functionals is their ability to reduce to the Thomas-Fermi functional in the regions of high density and to the von Weizsäcker functional…
eQE 2.0: Subsystem DFT Beyond GGA Functionals
Wenhui Mi, Xuecheng Shao, Alessandro Genova +2
By adopting a divide-and-conquer strategy, subsystem-DFT (sDFT) can dramatically reduce the computational cost of large-scale electronic structure calculations. The key ingredients…
An Efficient DFT Solver for Nanoscale Simulations and Beyond
Xuecheng Shao, Wenhui Mi, Michele Pavanello
We present the One-orbital Ensemble Self-Consistent Field (OE-SCF) method, an {alternative} orbital-free DFT solver that extends the applicability of DFT to system sizes beyond the…
DFTpy: An efficient and object-oriented platform for orbital-free DFT simulations
Xuecheng Shao, Kaili Jiang, Wenhui Mi +2
In silico materials design is hampered by the computational complexity of Kohn-Sham DFT, which scales cubically with the system size. Owing to the development of new-generation kin…
A linear scaling method to evaluate the ion-electron potential of crystalline solids
Xuecheng Shao, Wenhui Mi, Qiang Xu +2
We propose a simple linear scaling expression in reciprocal space for evaluating the ion--electron potential of crystalline solids. The expression replaces the long-range ion--elec…
ATLAS: A Real-Space Finite-Difference Implementation of Orbital-Free Density Functional Theory
Wenhui Mi, Xuecheng Shao, Chuanxun Su +8
Orbital-free density functional theory (OF-DFT) is a promising method for large-scale quantum mechanics simulation as it provides a good balance of accuracy and computational cost.…