DeepModeling

Since the release of the DeePTB v1 version[1], which supports the prediction of Slater-Koster tight-binding (TB) parameters, in Nature Communications in 2023, the DeePTB project has achieved a series of breakthroughs. Subsequently, the DeePTB team introduced the E3 equivariant version[2]. Its core method, the E3 equivariant representation based on Strictly Localized Equivariant Message-passing (SLEM), has made a breakthrough in the processing of quantum operator matrices, enabling more accurate and efficient representation of key quantum operators such as the Kohn-Sham (KS) Hamiltonian, density matrix, and overlap integral. The related research has been accepted as a Spotlight article at the ICLR 2025 conference. On this basis, the team further combined the DeePTB method with the Non-equilibrium Green's Function (NEGF) method to introduce the AI-accelerated quantum transport simulation framework DeePTB-NEGF[3]. This framework significantly improves the computational efficiency of quantum transport simulations. It has been successfully applied to transport calculations in high-throughput and large-scale scenarios, providing a new and efficient tool for quantum transport theoretical research and semiconductor device simulations.

The research group led by Shi Liu from the Department of Physics, School of Science, Westlake University has made the latest progress in revealing the mechanism by which interstitial doping reduces the coercive field of ferroelectric hafnium oxide. On February 4, the research results were published in Physical Review Letters under the title "Origin of Interstitial Doping Induced Coercive Field Reduction in Ferroelectric Hafnia".

In this study, the research group led by Shi Liu comprehensively used first-principles calculations and deep potential-based molecular dynamics to reveal the relationship between interstitial doping and the coercive field of ferroelectric hafnium oxide (HfO₂). It was found that interstitial hafnium doping significantly reduces the energy difference between the polar orthorhombic O phase (space group: Pca2₁) and the tetragonal T phase, thereby reducing the polarization reversal energy barrier and the coercive field. Compared with the theoretical model proposed in previous studies, which suggested that doping-induced rhombohedral R phase reduces the coercive field, this study proposed that the low-doped O phase can better explain the experimental observations of interstitial-doped hafnium-based thin films. Large-scale molecular dynamics simulations based on deep potential further indicated that interstitial hafnium doping can induce the formation of mobile Pbcn-type domain walls, thus reducing the polarization reversal electric field to less than 1 MV/cm. In addition, first-principles calculations revealed a negative correlation between the polarization reversal energy barrier and the radius of the doped atom, and several potential interstitial doping atoms that can effectively reduce the coercive field were screened out.

In machine learning potential development, it is challenging to sample complex chemical spaces with a small amount of data. The research group led by Dongping Chen from Beijing Institute of Technology constructed a feature dataset with low redundancy and low data requirements based on a data dimensionality reduction method. This dataset was combined with the DeePMD method to develop a machine learning potential with high accuracy, high efficiency, and wide application conditions, aiming to describe the interactions in aluminum-lithium (Al-Li) alloys and ammonium perchlorate (AP) interfaces. The relevant research results were published in the journal Journal of Chemical Theory and Computation under the title "Minimizing Redundancy and Data Requirements of Machine Learning Potential: A Case Study in Interface Combustion" (DOI: 10.1021/acs.jctc.4c00587)[1]. Xiaoya Chang, a doctoral student, is the first author.

In the field of drug research and development, accurately understanding the interactions between proteins and ligands is of great significance. It not only facilitates the discovery of new drugs but also provides a crucial basis for optimizing existing drugs. For a long time, the scarcity of high-quality data and the limitations of traditional research methods have greatly hindered the development of this field. Recently, researchers from the Institute of Biomedical Sciences at Tsinghua University and the Beijing Institute of Life Sciences have constructed the BindingNet v2 dataset. By combining it with the Uni-Mol model, they have made remarkable progress in structure-based drug design, especially in the task of predicting ligand binding sites of low-similarity molecules, where its performance exceeds that of AlphaFold3. The related research results were published in npj drug discovery under the title "Augmented BindingNet dataset for enhanced ligand binding pose predictions using deep learning". (doi: doi.org/10.1038/s44386 - 024 - 00003 - 0)

Recently, Dr. Junxiong Hu, a senior postdoctoral researcher at the National University of Singapore, and the research group of Professor Ariando, in collaboration with Professor Song Liu from Hunan University, Dr. Xudong Zhu, a postdoctoral researcher at the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, and others, have made important progress in the field of the interaction between two-dimensional materials and complex oxides. This study focused on the influence of the graphene layer on the formation of oxygen vacancies in SrTiO₃ (STO) during high-temperature annealing. Combined with Raman spectroscopy, X-ray photoelectron spectroscopy, and photoluminescence spectroscopy studies, it was proved that the graphene layer can effectively regulate the formation of oxygen vacancies in STO. The researchers used the open-source density functional theory software ABACUS (Atomic Calculator) to further reveal the principle that the multilayer graphene coating suppresses the formation of oxygen vacancies in STO by increasing the energy barrier for the outward diffusion of oxygen atoms. The research result "Tuning oxygen vacancies in complex oxides using 2D layered materials" has been published in the 2D Materials journal. This study proposes a new approach to regulating the physical properties of complex oxides by designing the interface of two-dimensional materials.

Recently, a research team led by Professor Yi He and Associate Researcher Nan Xu from Zhejiang University/Zhejiang University Quzhou Institute used the deep potential molecular dynamics (DeePMD) method to conduct an in-depth study of the lithium-ion diffusion mechanism in the solid electrolyte Li₆PS₅Cl. The relevant research results were published in the well-known materials journal Chemistry of Materials under the title "Insights into the Atomic Mechanism of Lithium-Ion Diffusion in Li₆PS₅Cl via a Machine Learning Potential" (DOI: 10.1021/acs.chemmater.4c01152), with master student Jiafeng Chen as the first author.

Recently, the research group led by Mingxing Chen from the College of Physics and Electronic Science at Hunan Normal University investigated the evolution of flat bands in the MoSe₂/WSe₂ moiré lattices of transition metal dichalcogenides using deep learning potential and Hamiltonian models. In this study, they employed the domestic first-principles software ABACUS, which is based on the linear combination of atomic orbitals (LCAO) basis set, to prepare machine learning datasets. Subsequently, they used DeepH, based on E3 equivariant graph neural networks, to train machine learning models for predicting the moiré lattice Hamiltonian. Diagonalizing the machine learning Hamiltonian yielded the electronic structure of the moiré system. Thereafter, they utilized the self-developed band unfolding program KPROJ to project the wavefunctions of the moiré supercell onto the k-points of the primitive cell and obtained the unfolded band structure, which was used to study the influence of the twist angle on the band structure of transition metal dichalcogenides. This work revealed that the flat bands originating from the valence band edge can stem from the Γ and K valleys. When the H-stacked MoSe₂/WSe₂ moiré lattice has a twist angle of 3.89˚ and spin-orbit coupling (SOC) is not considered, a flat band with a bandwidth of approximately 5 meV appears below the valence band edge at the K point. Subsequently, as the twist angle decreases, this flat band rapidly shifts upward and becomes approximately 20 meV higher than the valence band at the K point. When the twist angle further decreases to 1.7˚, multiple flat bands emerge. The research found that spin-orbit coupling leads to a large spin splitting, comparable to that in the untwisted system (approximately 0.45 eV), and is almost independent of twisting and stacking. Therefore, in the presence of spin-orbit coupling, the flat band in the K valley remains at the top of the valence band. The band unfolding results indicate that the flat bands formed by the Γ valley and the K valley exhibit distinct responses to the twist angle. The Γ valley flat band is highly sensitive to the interlayer coupling and thus rapidly shifts upward as the twist angle decreases. Conversely, the K valley flat band has a weaker dependence on the interlayer coupling and is primarily affected by structural reconstruction. Hence, a relatively small angle (2.13˚) is required to generate the K valley flat band. As the twist angle decreases, this flat band transforms from a honeycomb lattice to a triangular lattice. The relevant research, titled "Evolution of flat bands in MoSe₂/WSe₂ moiré lattices: A study combining machine learning and band unfolding methods", was published in Physical Review B [Phys. Rev. B 110, 235410 (2024)]. Doctoral students Shengguo Yang and Jiaxin Chen are the first and second authors respectively, and Mingxing Chen is the corresponding author.

Recently, the research group led by Shi Liu from the Department of Physics, School of Science, Westlake University, based on their previous work "Modular development of deep potential" (ModDP) [1], utilized the deep potential of the solid solution Pb_x Sr_{1 - x} TiO₃ to simulate the full - composition superlattices of the (PbTiO₃)₁₀/(Pb_x Sr_{1 - x} TiO₃)₁₀ system within the range of 0 ≤ x ≤ 1. This revealed a rich phase diagram derived from topological structures. In this study, the researchers took the typical system of (PbTiO₃)₁₀/(Pb_x Sr_{1 - x} TiO₃)₁₀ as a starting point to simulate the special phase transition of vortex domains in the superlattice during the heating process, namely, the ferroelectric - like - antiferroelectric - like - paraelectric phase transition. Moreover, the ferroelectric - like phase and the antiferroelectric - like phase can be respectively regulated under an external electric field, thus achieving two different types of electric hysteresis loops. By introducing Pb doping into the layer, the researchers also found that due to the weakening of the depolarization field, a topological phase transition from the vortex state to the skyrmion state can be induced. The relevant research results, titled "Topological phase transitions in perovskite superlattices driven by temperature, electric field, and doping", were published in Physical Review B [2]. Doctoral student Jiyuan Yang is the first author.

Recently, doctoral student Yu Liu and Researcher Mohan Chen from the AI for Science Institute, Beijing and Peking University, used the domestic open-source density functional theory software ABACUS to conduct an in-depth study on the impact of multiple hyperuniform long-range order on the electronic properties of high-entropy two-dimensional material MXene. The two-dimensional materials optimized by density functional theory contain more than 1,500 atoms, indicating that ABACUS is suitable for handling large material systems. The relevant research results, titled "Multihyperuniformity in high-entropy MXenes", were published in Appl. Phys. Lett. 126, 013101 (2025) and were selected as Editor's pick by the editor (https://doi.org/10.1063/5.0246719).

DeepFlame is an open-source platform for combustion fluid calculations developed for the AI for Science era [1 - 3]. It aims to break through the long-standing application implementation challenges of traditional Computational Fluid Dynamics (CFD) in the field of combustion fluids. Since its release, DeepFlame has attracted the interest and attention of both academia and industry, and has drawn a group of outstanding developers and users, providing continuous impetus for the sustainable development of DeepFlame.

In recent years, flow and combustion problems driven by buoyancy, such as fire research in fields like batteries, have garnered increasingly extensive attention from academia and industry. The DeepFlame team has seized on this hot topic. Referring to the fireFoam solver in OpenFOAM, they constructed the dfBuoyancyFoam solver based on the PIMPLE algorithm. This solver is suitable for solving turbulent flow and diffusion flame problems driven by buoyancy, expanding the application scenarios of DeepFlame. Moreover, in the development trend of new-generation CPU supercomputers based on the ARM architecture, the newly released version has been comprehensively adapted and optimized for DeepFlame on Kunpeng and Fujitsu hardware, significantly enhancing computational efficiency and parallel scalability.