Publications (64)
First-principles study of the infrared spectrum in liquid water from a systematically improved description of H-bond network
Jianhang Xu, Mohan Chen, Cui Zhang +1
An accurate ab initio theory of the H-bond structure of liquid water requires a high-level exchange correlation approximation from density functional theory. Based on the liquid st…
DPA-2: a large atomic model as a multi-task learner
Duo Zhang, Xinzijian Liu, Xiangyu Zhang +40
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demo…
First-principles molecular dynamics study of deuterium diffusion in liquid tin
Xiaohui Liu, Daye Zheng, Xinguo Ren +2
Understanding the retention of hydrogen isotopes in liquid metals, such as lithium and tin, is of great importance in designing a liquid plasma-facing component in fusion reactors.…
Isotope effects on molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on SCAN functional
Jianhang Xu, Chunyi Zhang, Linfeng Zhang +3
Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (HO) and heavy water (DO) within the isothermal-isobar…
Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Towards a Universal Model
Jianchuan Liu, Xingchen Zhang, Tao Chen +4
Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems wi…
Stone-Wales Defects Preserve Hyperuniformity in Amorphous Two-Dimensional Materials
Duyu Chen, Yu Zheng, Lei Liu +4
Crystalline two-dimensional (2D) materials such as graphene possess unique physical properties absent in their bulk form, enabling many novel device applications. Yet, little is kn…
Structural, Electronic, and Dynamical Properties of Liquid Water by ab initio Molecular Dynamics based on SCAN Functional within the Canonical Ensemble
Lixin Zheng, Mohan Chen, Zhaoru Sun +4
We perform ab initio molecular dynamics (AIMD) simulation of liquid water in the canonical ensemble at ambient conditions using the SCAN meta-GGA functional approximation, and carr…
Understanding the Density Maximum of Water with Machine Learned Potentials
Yizhi Song, Renxi Liu, Chunyi Zhang +5
After melting, at ambient pressure, the density of water continues to increase with temperature until it reaches a maximum around 4 °C. For nearly a century, this phenomenon has b…
Disordered Hyperuniform Solid State Materials
Duyu Chen, Houlong Zhuang, Mohan Chen +3
Disordered hyperuniform (DHU) states are recently discovered exotic states of condensed matter. DHU systems are similar to liquids or glasses in that they are statistically isotrop…
Deep neural network for the dielectric response of insulators
Linfeng Zhang, Mohan Chen, Xifan Wu +3
We introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab-initio densi…
Enhanced Knowledge Injection for Radiology Report Generation
Qingqiu Li, Jilan Xu, Runtian Yuan +5
Automatic generation of radiology reports holds crucial clinical value, as it can alleviate substantial workload on radiologists and remind less experienced ones of potential anoma…
Why does hydronium diffuse faster than hydroxide in liquid water?
Mohan Chen, Lixin Zheng, Biswajit Santra +5
Proton transfer via hydronium and hydroxide ions in water is ubiquitous. It underlies acid-base chemistry, certain enzyme reactions, and even infection by the flu. Despite two-cent…
Systematically improvable optimized atomic basis sets for {\it ab inito} calculations
Mohan Chen, G-C Guo, Lixin He
We propose a unique scheme to construct fully optimized atomic basis sets for density-functional calculations. The shapes of the radial functions are optimized by minimizing the {\…
Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms
Zhuoqiang Guo, Denghui Lu, Yujin Yan +11
High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of \textit{ab initio} m…
DP Compress: a Model Compression Scheme for Generating Efficient Deep Potential Models
Denghui Lu, Wanrun Jiang, Yixiao Chen +4
Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structur…
Systematically Improvable Numerical Atomic Orbital Basis Using Contracted Truncated Spherical Waves
Yike Huang, Zuxin Jin, Linfeng Zhang +3
To solve the Kohn-Sham equation within the framework of density functional theory, we develop a scheme to construct numerical atomic orbital (NAO) basis sets by contracting truncat…
Modeling the High-Pressure Solid and Liquid Phases of Tin from Deep Potentials with ab initio Accuracy
Tao Chen, Fengbo Yuan, Jianchuan Liu +4
Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties of Sn are sensitive to pressures. We develop machine-learning-ba…
Electron-hole theory of the effect of quantum nuclei on the x-ray absorption spectra of liquid water
Zhaoru Sun, Lixin Zheng, Mohan Chen +3
Electron-hole excitation theory is used to unveil the role of nuclear quantum effects on the X-ray absorption spectral signatures of water, whose structure is computed via path-int…
Extending Nonlocal Kinetic Energy Density Functionals to Isolated Systems via a Density-Functional-Dependent Kernel
Liang Sun, Mohan Chen
The Wang-Teter-like nonlocal kinetic energy density functional (KEDF) in the framework of orbital-free density functional theory, while successful in some bulk systems, exhibits a…
Machine learning based nonlocal kinetic energy density functional for simple metals and alloys
Liang Sun, Mohan Chen
Developing an accurate kinetic energy density functional (KEDF) remains a major hurdle in orbital-free density functional theory. We propose a machine learning based physical-const…
Roadmap for warm dense matter physics
Jan Vorberger, Frank Graziani, David Riley +71
This roadmap presents the state-of-the-art, current challenges and near future developments anticipated in the thriving field of warm dense matter physics. Originating from strongl…
Stress-driven dynamic evolution of core-shell structured cavities with H and He in BCC-Fe under fusion conditions
Jin Wang, Fengping Luo, Yiheng Chen +11
Understanding the dynamic behavior of microstructures formed under fusion conditions is critical for designing high-performance structural materials for fusion reactors. Under fusi…
Self-energy effects and energy band theory for warm dense matter
Chang Gao, Shen Zhang, X. T. He +4
The energy band structures caused by self-energy shifting that results in bound energy levels broadening and merging in warm dense aluminum and beryllium are observed. An energy ba…
Topology-Driven Anti-Entanglement Control for Soft Robots
Haoyang Le, Shengxuan Wang, Mohan Chen +1
In the field of precision manufacturing in complex constrained environments, the role of soft robots is increasingly prominent, and the realization of anti-winding control based on…
Integrating Deep-Learning-Based Magnetic Model and Non-Collinear Spin-Constrained Method: Methodology, Implementation and Application
Daye Zheng, Xingliang Peng, Yike Huang +8
We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic…
DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials
Wenfei Li, Qi Ou, Yixiao Chen +9
Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanica…
X-ray absorption of liquid water by advanced ab initio methods
Zhaoru Sun, Mohan Chen, Lixin Zheng +7
Oxygen K-edge X-ray absorption spectra of liquid water are computed based on the configurations from advanced ab initio molecular dynamics simulations, as well as an electron excit…
Multihyperuniformity in high entropy MXenes
Yu Liu, Mohan Chen
MXenes are a large family of two-dimensional transition metal carbides and nitrides that possess excellent electrical conductivity, high volumetric capacitance, great mechanical pr…
Retention and Recycling of Deuterium in Liquid Lithium-Tin Slab Studied by First-Principles Molecular Dynamics
Daye Zheng, Zhen-Xiong Shen, Mohan Chen +2
Understanding the retention and recycling of hydrogen isotopes in liquid metal plasma-facing materials such as liquid Li, Sn, and Li-Sn are of fundamental importance in designing m…
Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
Weile Jia, Han Wang, Mohan Chen +5
For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles. However, most AIMD application…
Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics
Yuzhi Zhang, Chang Gao, Linfeng Zhang +2
Simulating warm dense matter that undergoes a wide range of temperatures and densities is challenging. Predictive theoretical models, such as quantum-mechanics-based first-principl…
Investigating CO Adsorption on Cu(111) and Rh(111) Surfaces Using Machine Learning Exchange-Correlation Functionals
Xinyuan Liang, Renxi Liu, Mohan Chen
The "CO adsorption puzzle", a persistent failure of utilizing generalized gradient approximations (GGA) in density functional theory to replicate CO's experimental preference for t…
Effects of Non-local Pseudopotentials on the Electrical and Thermal Transport Properties of Aluminum: A Density Functional Theory Study
Qianrui Liu, Mohan Chen
Accurate prediction of electron transport coefficients is crucial for understanding warm dense matter. Utilizing the density functional theory (DFT) with the Kubo-Greenwood formula…
A Deep Learning Framework for the Electronic Structure of Water: Towards a Universal Model
Xinyuan Liang, Renxi Liu, Mohan Chen
Accurately modeling the electronic structure of water across scales, from individual molecules to bulk liquid, remains a grand challenge. Traditional computational methods face a c…
Stabilization of highly polar BiFeO-like structure: a new interface design route for enhanced ferroelectricity in artificial perovskite superlattices
Hongwei Wang, Jianguo Wen, Dean J. Miller +5
In ABO3 perovskites, oxygen octahedron rotations are common structural distortions that can promote large ferroelectricity in BiFeO3 with an R3c structure [1], but suppress ferroel…
GPU Acceleration of Numerical Atomic Orbitals-Based Density Functional Theory Algorithms within the ABACUS package
Haochong Zhang, Zichao Deng, Yu Liu +4
With the fast developments of high-performance computing, first-principles methods based on quantum mechanics play a significant role in materials research, serving as fundamental…
Ab initio theory and modeling of water
Mohan Chen, Hsin-Yu Ko, Richard C. Remsing +8
Water is of the utmost importance for life and technology. However, a genuinely predictive ab initio model of water has eluded scientists. We demonstrate that a fully ab initio app…
Electrical and thermal transport properties of medium-entropy SiyGeySnx alloys
Duo Wang, Lei Liu, Mohan Chen +1
Electrical and thermal transport properties of disordered materials have long been of both theoretical interest and engineering importance. As a new class of materials with an intr…
Structural and Dynamic Properties of Solvated Hydroxide and Hydronium Ions in Water from Ab Initio Modeling
Renxi Liu, Chunyi Zhang, Xinyuan Liang +3
Predicting the asymmetric structure and dynamics of solvated hydroxide and hydronium in water has been a challenging task from ab initio molecular dynamics (AIMD). The difficulty m…
Applying Space-Group Symmetry to Speed up Hybrid-Functional Calculations within the Framework of Numerical Atomic Orbitals
Yu Cao, Min-Ye Zhang, Peize Lin +2
Building upon the efficient implementation of hybrid density functionals (HDFs) for large-scale periodic systems within the framework of numerical atomic orbital bases using the lo…
Electronic structure interpolation via atomic orbitals
Mohan Chen, G-C Guo, Lixin He
We present an efficient scheme for accurate electronic structure interpolations based on the systematically improvable optimized atomic orbitals. The atomic orbitals are generated…
Enhanced Electronic Transport in Disordered Hyperuniform Two-Dimensional Amorphous Silica
Yu Zheng, Lei Liu, Hanqing Nan +8
Disordered hyperuniformity (DHU) is a recently proposed new state of matter, which has been observed in a variety of classical and quantum many-body systems. DHU systems are charac…
Text-Promptable Propagation for Referring Medical Image Sequence Segmentation
Runtian Yuan, Mohan Chen, Jilan Xu +6
Referring Medical Image Sequence Segmentation (Ref-MISS) is a novel and challenging task that aims to segment anatomical structures in medical image sequences (\emph{e.g.} endoscop…
Thermal and Electrical Conductivities of Aluminum Up to 1000 eV: A First-Principles Prediction
Qianrui Liu, Xiantu He, Mohan Chen
Accurate prediction of the thermal and electrical conductivities of materials under extremely high temperatures is essential in high-energy-density physics. These properties govern…
Accelerating Atomic Orbital-based Electronic Structure Calculation via Pole Expansion and Selected Inversion
Lin Lin, Mohan Chen, Chao Yang +1
We describe how to apply the recently developed pole expansion and selected inversion (PEXSI) technique to Kohn-Sham density function theory (DFT) electronic structure calculations…
Thermal Transport by Electrons and Ions in Warm Dense Aluminum: A Combined Density Functional Theory and Deep Potential Study
Qianrui Liu, Junyi Li, Mohan Chen
We propose an efficient scheme, which combines density functional theory (DFT) with deep potentials (DP), to systematically study the convergence issues of the computed electronic…
Implementation of the SCAN Exchange-Correlation Functional with Numerical Atomic Orbitals
Renxi Liu, Daye Zheng, Xinyuan Liang +3
Kohn-Sham density functional theory (DFT) is nowadays widely used for electronic structure theory simulations, and the accuracy and efficiency of DFT rely on approximations of the…
A Deep Learning Potential for Accurate Shock Response Simulations in Tin
Yixin Chen, Xiaoyang Wang, Wanghui Li +2
Tin (Sn) plays a crucial role in studying the dynamic mechanical responses of ductile metals under shock loading. Atomistic simulations serves to unveil the nano-scale mechanisms f…
Dynamic Graph Message Passing Networks for Visual Recognition
Li Zhang, Mohan Chen, Anurag Arnab +2
Modelling long-range dependencies is critical for scene understanding tasks in computer vision. Although convolution neural networks (CNNs) have excelled in many vision tasks, they…
Structural and mechanical properties of W-Cu compounds characterized by a neural-network-based potential
Jianchuan Liu, Tao Chen, Sheng Mao +1
Tungsten-copper (W-Cu) compounds are widely utilized in various industrial fields due to their exceptional mechanical properties. In this study, we have developed a neural-network-…
Evolution of cavities in BCC-Fe with coexisting H and He under fusion environments
Jin Wang, Fengping Luo, Tao Zheng +10
In the fusion environment, understanding the synergistic effects of transmutation-produced hydrogen (H), helium (He), and irradiation-induced displacement damage in iron-based allo…
A Unified Heterogeneous Implementation of Numerical Atomic Orbitals-Based Real-Time TDDFT within the ABACUS Package
Taoni Bao, Yuanbo Li, Zichao Deng +6
We present a unified heterogeneous computing framework for real-time time-dependent density functional theory (RT-TDDFT) based on numerical atomic orbitals (NAOs), implemented in t…
Multi-channel machine learning based nonlocal kinetic energy density functional for semiconductors
Liang Sun, Mohan Chen
The recently proposed machine learning-based physically-constrained nonlocal (MPN) kinetic energy density functional (KEDF) can be used for simple metals and their alloys [Phys. Re…
Characterization of the Hydrogen-Bond Network in High-Pressure Water by Deep Potential Molecular Dynamics
Renxi Liu, Mohan Chen
The hydrogen-bond (H-bond) network of high-pressure water is investigated by neural-network-based molecular dynamics (MD) simulations with the first-principles accuracy. The static…
Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter
Tao Chen, Qianrui Liu, Yu Liu +2
In traditional finite-temperature Kohn-Sham density functional theory (KSDFT), the well-known orbitals wall restricts the use of first-principles molecular dynamics methods at extr…
Disordered Hyperuniform Quasi-1D Materials
Duyu Chen, Yu Liu, Yu Zheng +3
Carbon nanotubes are quasi-one-dimensional systems that possess superior transport, mechanical, optical, and chemical properties. In this work, we generalize the notion of disorder…
Plane-Wave-Based Stochastic-Deterministic Density Functional Theory for Extended Systems
Qianrui Liu, Mohan Chen
Traditional finite-temperature Kohn-Sham density functional theory (KSDFT) has an unfavorable scaling with respect to the electron number or at high temperatures. The evaluation of…
ABACUS: An Electronic Structure Analysis Package for the AI Era
Weiqing Zhou, Daye Zheng, Qianrui Liu +55
ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It…
Truncated Non-Local Kinetic Energy Density Functionals for Simple Metals and Silicon
Liang Sun, Yuanbo Li, Mohan Chen
Adopting an accurate kinetic energy density functional (KEDF) to characterize the noninteracting kinetic energy within the framework of orbital-free density functional theory (OFDF…
Iterative minimization in reduced density matrix functional theory for periodic systems
Kai Luo, Jingang Han, Peize Lin +3
The paper presents a basis‑independent, planewave implementation of reduced density matrix functional theory for periodic solids, using iterative minimization techniques to optimiz…
Large-scale ab initio simulations based on systematically improvable atomic basis
Pengfei Li, Xiaohui Liu, Mohan Chen +5
We present a first-principles computer code package (ABACUS) that is based on density functional theory and numerical atomic basis sets. Theoretical foundations and numerical techn…
Modeling liquid water by climbing up Jacob's ladder in density functional theory facilitated by using deep neural network potentials
Chunyi Zhang, Fujie Tang, Mohan Chen +5
Within the framework of Kohn-Sham density functional theory (DFT), the ability to provide good predictions of water properties by employing a strongly constrained and appropriately…
Exploring the energy landscape of aluminas through machine learning interatomic potential
Lei Zhang, Wenhao Luo, Renxi Liu +3
Aluminum oxide (alumina, AlO) exists in various structures and has broad industrial applications. While the crystal structure of -AlO is well-established, those…
86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy
Denghui Lu, Han Wang, Mohan Chen +6
We present the GPU version of DeePMD-kit, which, upon training a deep neural network model using ab initio data, can drive extremely large-scale molecular dynamics (MD) simulation…