arXiv subjects

Mohan Chen

Publications and source records attributed to Mohan Chen.

At least 37 records · Page 2Linked to original sources

arXiv2025

Structural and mechanical properties of W-Cu compounds characterized by a neural-network-based potential

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-based deep potential (DP) model that covers a wide range of temperatures, ranging from 0 to 3,000 K, and pressures, varying from 0 to 10 GPa. This study presents a model trained using density functional theory data for full concentration CuxW100-x compounds. Through this model, we systematically investigate the structural and mechanical properties of W-Cu alloys and have the following findings. First, the bulk modulus (B) and Young's modulus (E) of W-Cu alloys exhibit a linear decline as the Cu content increases, indicating a softening trend in the CuxW100-x compounds as the Cu concentration rises. Second, a higher Cu content results in higher critical strain and lower critical stress for these compounds. A brittle-to-ductile transition in the deformation mode predicted is predicted at around 37.5 at. % Cu content. Third, tensile loading tests in the W-Cu gradient structure reveal that Cu-poor region serves as a barrier, hindering shear band propagation while promoting new shear band formation in the Cu-rich region. The above results from the DP model are anticipated to aid in exploring the physical mechanisms underlying the complex phenomena of W-Cu systems and contribute to the advancement of methodologies for materials simulation.

cond-mat.mtrl-sci↗

arXiv2024

Exploring the energy landscape of aluminas through machine learning interatomic potential

Aluminum oxide (alumina, Al$_2$O$_3$) exists in various structures and has broad industrial applications. While the crystal structure of $α$-Al$_2$O$_3$ is well-established, those of transitional aluminas remain highly debated. In this study, we propose a universal machine learning interatomic potential (MLIP) for aluminas, trained using the neuroevolution potential (NEP) approach. The dataset is constructed through iterative training and farthest point sampling, ensuring the generation of the most representative configurations for an exhaustive sampling of the potential energy surface. The accuracy and generality of the potential are validated through simulations under a wide range of conditions, including high temperatures and pressures. A phase diagram is presented that includes both transitional aluminas and $α$-Al$_2$O$_3$ based on the NEP. We also successfully extrapolate the phase diagram of aluminas under extreme conditions ([0, 4000] K and [0, 200] GPa ranges of temperature and pressure, respectively), while maintaining high accuracy in describing their properties under more moderate conditions. Furthermore, combined with our developed structure search workflow, the NEP provides an evaluation of existing $γ$-Al$_2$O$_3$ structure models. The NEP developed in this work enables highly accurate dynamic simulations of various aluminas on larger scales and longer timescales, while also offering new insights into the study of transitional aluminas structures.

cond-mat.mtrl-sci↗

arXiv2024

Multihyperuniformity in high entropy MXenes

MXenes are a large family of two-dimensional transition metal carbides and nitrides that possess excellent electrical conductivity, high volumetric capacitance, great mechanical properties, and hydrophilicity. In this work, we generalize the concept of multihyperuniformity (MH), an exotic state that can exist in a disordered multi-component system, to two-dimensional materials MXenes. Disordered hyperuniform systems possess an isotropic local structure that lacks traditional translational and orientational order, yet they completely suppress infinite-wavelength density fluctuations as in perfect crystals and, in this sense, possess a hidden long-range order. In particular, we evaluate the static structure factor of the individual components present in the high entropy (HE) MXene experimental sample TiVCMoCr based on high-solution SEM imaging data, which suggests this HE MXene system is at least effectively multihyperuniform. We then devise a packing algorithm to generate multihyperuniform models of HE MXene systems. The MH HE MXenes are predicted to be energetically more stable compared to the prevailing (quasi)random models of the HE MXenes due to the hidden long-range order. Moreover, the MH structure exhibits a distinctly smaller lattice distortion, which has a vital effect on the electronic properties of HE MXenes, such as the density of states and charge distribution. This systematic study of HE MXenes strengthens our fundamental understanding of these systems, and suggests possible exotic physical properties, as endowed by the multihyperuniformity.

cond-mat.mes-hall↗

arXiv2024

GPU Acceleration of Numerical Atomic Orbitals-Based Density Functional Theory Algorithms within the ABACUS package

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 tools for predicting and analyzing various properties of materials. However, the inherent complexity and substantial computational demands of first-principles algorithms, such as density functional theory, limit their use in larger systems. The rapid development of heterogeneous computing, particularly General-Purpose Graphics Processing Units (GPGPUs), has heralded new prospects for enhancing the performance and cost-effectiveness of first-principles algorithms. We utilize GPGPUs to accelerate the electronic structure algorithms in Atomic-orbital Based Ab-initio Computation at USTC (ABACUS), a first-principles computational package based on the linear combination of atomic orbitals (LCAO) basis set. We design algorithms on GPGPU to efficiently construct and diagonalize the Hamiltonian of a given system, including the related force and stress calculations. The effectiveness of this computational acceleration has been demonstrated through calculations on twisted bilayer graphene with the system size up to 10,444 atoms.

cond-mat.mtrl-sci↗

arXiv2024

Multi-channel machine learning based nonlocal kinetic energy density functional for semiconductors

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. Rev. B 109, 115135 (2024)]. However, the MPN KEDF does not perform well for semiconductors. Here we propose a multi-channel MPN (CPN) KEDF, which extends the MPN KEDF to semiconductors by integrating information collected from multiple channels, with each channel featuring a specific length scale in real space. The CPN KEDF is systematically tested on silicon and binary semiconductors. We find that the multi-channel design for KEDF is beneficial for machine-learning-based models in capturing the characteristics of semiconductors, particularly in handling covalent bonds. In particular, the CPN5 KEDF, which utilizes five channels, demonstrates excellent accuracy across all tested systems. These results offer a new path for generating KEDFs for semiconductors.

cond-mat.mtrl-sci↗

arXiv2024

DPA-2: a large atomic model as a multi-task learner

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

physics.chem-ph↗

arXiv2024

Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Towards a Universal Model

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 with ab initio accuracy. Still, the community awaits universal inter-atomic models that can be applied to a wide range of materials without tuning neural network parameters. We develop a unified deep-learning inter-atomic potential (the DPA-Semi model) for 19 semiconductors ranging from group IIB to VIA, including Si, Ge, SiC, BAs, BN, AlN, AlP, AlAs, InP, InAs, InSb, GaN, GaP, GaAs, CdTe, InTe, CdSe, ZnS, and CdS. In addition, independent deep potential models for each semiconductor are prepared for detailed comparison. The training data are obtained by performing density functional theory calculations with numerical atomic orbitals basis sets to reduce the computational costs. We systematically compare various properties of the solid and liquid phases of semiconductors between different machine-learning models. We conclude that the DPA-Semi model achieves GGA exchange-correlation functional quality accuracy and can be regarded as a pre-trained model towards a universal model to study group IIB to VIA semiconductors.

cond-mat.mtrl-sci↗

arXiv2024

Machine learning based nonlocal kinetic energy density functional for simple metals and alloys

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-constrained nonlocal (MPN) KEDF and implement it with the usage of the bulk-derived local pseudopotentials and plane wave basis sets in the ABACUS package. The MPN KEDF is designed to satisfy three exact physical constraints: the scaling law of electron kinetic energy, the free electron gas limit, and the non-negativity of Pauli energy density. The MPN KEDF is systematically tested for simple metals, including Li, Mg, Al, and 59 alloys. We conclude that incorporating nonlocal information for designing new KEDFs and obeying exact physical constraints are essential to improve the accuracy, transferability, and stability of ML-based KEDF. These results shed new light on the construction of ML-based functionals.

cond-mat.mtrl-sci↗

arXiv2024

Effects of Non-local Pseudopotentials on the Electrical and Thermal Transport Properties of Aluminum: A Density Functional Theory Study

Accurate prediction of electron transport coefficients is crucial for understanding warm dense matter. Utilizing the density functional theory (DFT) with the Kubo-Greenwood formula is widely used to evaluate the electrical and thermal conductivities of electrons. By adding the non-local potential correction term that appears in the dynamic Onsager coefficient and using two different norm-conserving pseudopotentials, we predict the electrical and thermal conductivities of electrons for liquid Al (1000 K) and warm dense Al (0.2 to 10 eV). We systematically investigate the effects of non-local terms in the pseudopotentials and the frozen-core approximation on the conductivities. We find that taking into account the non-local potential correction and validating the frozen core approximation is essential for accurately calculating the electrical and thermal transport properties of electrons across a wide range of temperatures.

cond-mat.mtrl-sci↗

arXiv2023

Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter

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 extremely high temperatures. However, stochastic density functional theory (SDFT) can overcome the limitation. Recently, SDFT and its related mixed stochastic-deterministic density functional theory, based on the plane-wave basis set, have been implemented in the first-principles electronic structure software ABACUS [Phys. Rev. B 106, 125132(2022)]. In this study, we combine SDFT with the Born-Oppenheimer molecular dynamics (BOMD) method to investigate systems with temperatures ranging from a few tens of eV to 1000 eV. Importantly, we train machine-learning-based interatomic models using the SDFT data and employ these deep potential models to simulate large-scale systems with long trajectories. Consequently, we compute and analyze the structural properties, dynamic properties, and transport coefficients of warm dense matter. The abovementioned methods offer a new approach with first-principles accuracy to tackle various properties of warm dense matter.

physics.plasm-ph↗

arXiv2023

Enhanced Knowledge Injection for Radiology Report Generation

Automatic generation of radiology reports holds crucial clinical value, as it can alleviate substantial workload on radiologists and remind less experienced ones of potential anomalies. Despite the remarkable performance of various image captioning methods in the natural image field, generating accurate reports for medical images still faces challenges, i.e., disparities in visual and textual data, and lack of accurate domain knowledge. To address these issues, we propose an enhanced knowledge injection framework, which utilizes two branches to extract different types of knowledge. The Weighted Concept Knowledge (WCK) branch is responsible for introducing clinical medical concepts weighted by TF-IDF scores. The Multimodal Retrieval Knowledge (MRK) branch extracts triplets from similar reports, emphasizing crucial clinical information related to entity positions and existence. By integrating this finer-grained and well-structured knowledge with the current image, we are able to leverage the multi-source knowledge gain to ultimately facilitate more accurate report generation. Extensive experiments have been conducted on two public benchmarks, demonstrating that our method achieves superior performance over other state-of-the-art methods. Ablation studies further validate the effectiveness of two extracted knowledge sources.

cs.CV↗

arXiv2023

Characterization of the Hydrogen-Bond Network in High-Pressure Water by Deep Potential Molecular Dynamics

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 structure factors (SSFs) of water at three densities, i.e., 1, 1.115 and 1.24 g/cm3 are directly evaluated from 512-water MD trajectories, which are in quantitative agreement with the experiments. We propose a new method to decompose the computed SSF and identify the changes in SSF with respect to the changes in H-bond structures. We find a larger water density results in a higher probability for one or two non-H-bonded water molecules to be inserted into the inner shell, explaining the changes in the tetrahedrality of water under pressure. We predict that the structure of the accepting end of water molecules is more easily influenced by the pressure than the donating end. Our work sheds new light on explaining the SSF and H-bond properties in related fields.

physics.chem-ph↗

arXiv2023

Truncated Non-Local Kinetic Energy Density Functionals for Simple Metals and Silicon

Adopting an accurate kinetic energy density functional (KEDF) to characterize the noninteracting kinetic energy within the framework of orbital-free density functional theory (OFDFT) is challenging. We propose a new form of the non-local KEDF with a real-space truncation cutoff that satisfies the uniform electron gas limit and design KEDFs for simple metals and silicon. The new KEDFs are obtained by minimizing a residual function, which contains the differences in the total energy and charge density of several representative systems with respect to the Kohn-Sham DFT results. By systematically testing different cutoffs of the new KEDFs, we find that the cutoff plays a crucial role in determining the properties of metallic Al and semiconductor Si systems. We conclude that the new KEDF with a sufficiently long cutoff performs even better than some representative non-local KEDFs in some aspects, which sheds new light on optimizing the KEDFs in OFDFT to achieve better accuracy.

physics.comp-ph↗

arXiv2023

Implementation of the SCAN Exchange-Correlation Functional with Numerical Atomic Orbitals

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 exchange-correlation functional. By inclusion of the kinetic energy density $τ$, the meta-generalized-gradient approximation (meta-GGA) family of functionals achieves better accuracy and flexibility while retaining the efficiency of semi-local functionals. The SCAN meta-GGA functional has been proven to yield accurate results for solid and molecular systems. We implement meta-GGA functionals with both numerical atomic orbitals and plane wave basis in the ABACUS package. Apart from the exchange-correlation potential, we also discuss the evaluation of force and stress. To validate our implementation, we perform finite-difference tests and convergence tests with the SCAN meta-GGA functional. We further test water hexamers, weakly interacting molecules of the S22 dataset, as well as 13 semiconductors. The results show satisfactory agreements with previous calculations and available experimental values.

cond-mat.mtrl-sci↗

arXiv2023

Modeling the High-Pressure Solid and Liquid Phases of Tin from Deep Potentials with ab initio Accuracy

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-based deep potentials for Sn with pressures ranging from 0 to 50 GPa and temperatures ranging from 0 to 2000 K. In particular, we find the deep potential, which is obtained by training the ab initio data from density functional theory calculations with the state-of-the-art SCAN exchange-correlation functional, is suitable to characterize high-pressure phases of Sn. We systematically validate several structural and elastic properties of the α (diamond structure), β, bct, and bcc structures of Sn, as well as the structural and dynamic properties of liquid Sn. The thermodynamics integration method is further utilized to compute the free energies of the α, β, bct, and liquid phases, from which the deep potential successfully predicts the phase diagram of Sn including the existence of the triple-point that qualitatively agrees with the experiment.

cond-mat.mtrl-sci↗

arXiv2023

Thermal Transport by Electrons and Ions in Warm Dense Aluminum: A Combined Density Functional Theory and Deep Potential Study

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 thermal conductivity of warm dense Al (2.7 g/cm$^3$, temperatures ranging from 0.5 to 5.0 eV) with respect to the number of $k$-points, the number of atoms, the broadening parameter, the exchange-correlation functionals and the pseudopotentials. Furthermore, the ionic thermal conductivity is obtained by the Green-Kubo method in conjunction with DP molecular dynamics simulations, and we study the size effects in affecting the ionic thermal conductivity. This work demonstrates that the proposed method is efficient in evaluating both electronic and ionic thermal conductivities of materials.

physics.comp-ph↗

arXiv2022

Disordered Hyperuniform Solid State Materials

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 isotropic and lack conventional long-range translational and orientational order. On the other hand, they completely suppress normalized infinite-wavelength density fluctuations like crystals, and in this sense possess a hidden long-range order. Very recently, there are several exciting discoveries of disordered hyperuniformity in solid-state materials, including amorphous carbon nanotubes, amorphous 2D silica, amorphous graphene, defected transition metal dichalcogenides, defected pentagonal 2D materials, and medium/high-entropy alloys. It has been found the DHU states of these materials often possess a significantly lower energy than other disorder models, and can lead to unique electronic and thermal transport properties, which resulted from mechanisms distinct from those identified for their crystalline counterparts. For example, DHU states can enhance electronic transport in 2D amorphous silica; DHU medium/high-entropy alloys realize the Vegard's law, and possess enhanced electronic band gaps and thermal transport at low temperatures. These unique properties open up many promising potential device applications in optoelectronics and thermoelectrics. Here, we provide a focused review on these important new developments of hyperuniformity in solid-state materials, taking an applied and ``materials'' perspective, which complements the existing reviews on hyperuniformity in physical systems and photonic materials. Future directions and outlook are also provided, with a focus on the design and discovery of DHU quantum materials for quantum information science and engineering.

cond-mat.mtrl-sci↗

arXiv2022

Dynamic Graph Message Passing Networks for Visual Recognition

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 are still limited in capturing long-range structured relationships as they typically consist of layers of local kernels. A fully-connected graph, such as the self-attention operation in Transformers, is beneficial for such modelling, however, its computational overhead is prohibitive. In this paper, we propose a dynamic graph message passing network, that significantly reduces the computational complexity compared to related works modelling a fully-connected graph. This is achieved by adaptively sampling nodes in the graph, conditioned on the input, for message passing. Based on the sampled nodes, we dynamically predict node-dependent filter weights and the affinity matrix for propagating information between them. This formulation allows us to design a self-attention module, and more importantly a new Transformer-based backbone network, that we use for both image classification pretraining, and for addressing various downstream tasks (object detection, instance and semantic segmentation). Using this model, we show significant improvements with respect to strong, state-of-the-art baselines on four different tasks. Our approach also outperforms fully-connected graphs while using substantially fewer floating-point operations and parameters. Code and models will be made publicly available at https://github.com/fudan-zvg/DGMN2

cs.CV↗