DeepModeling

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.

Recently, teams led by Professor Yongbing Xu from Nanjing University, Professor Lin Miao from Southeast University, and Professor Lixin He from the Hefei Comprehensive National Science Center Institute of Artificial Intelligence collaborated. By combining Mott polarization measurement technology and first-principles calculations, they found that the two-dimensional vdw magnet CrTe2 has significantly enhanced spin polarization in its ultrathin limit. Especially at a thickness of 3 layers, the spin polarization rate reaches 23.4%. The researchers used the domestic open-source density functional theory software ABACUS (Atomic Abacus) + TB2J software to calculate in detail the magnetic anisotropy energy and interlayer exchange interaction of CrTe2 thin films at different thicknesses, highlighting the relevance of perpendicular magnetic anisotropy, interlayer interaction, and electron itinerant behavior to spin polarization at different film thicknesses. The research results "Substantially Enhanced Spin Polarization in Epitaxial CrTe2 Quantum Films" were published in the Advanced materials journal.

Paper link: https://onlinelibrary.wiley.com/doi/10.1002/adma.202411137

Recently, the experimental team led by Yuanyuan Shi from the University of Science and Technology of China collaborated with the theoretical team from the Hefei Comprehensive National Science Center Institute of Artificial Intelligence. They controlled the formation of conductive filaments by using a single layer of MoS₂ as an interlayer in the TiOₓ resistive switching layer of analog resistive random access memory (analog RRAM). This improved the device's switching uniformity, linearity, and symmetry. The voltage difference between switching cycles was only 1.28% and 1.7%. This structure also achieved high conductance modulation linearity and 64 conductance states (6-bit), and the on-chip training accuracy was as high as 93.02%. The researchers used the domestic open-source density functional theory software ABACUS (Atomic Abacus) to calculate the diffusion, migration, and defect structures of Ti ions on the MoS₂ surface and successfully explained the formation mechanism of conductive filaments in RRAM based on the TiOₓ/MoS₂–xOₓ structure induced by S vacancies. This work effectively improved the uniformity, linearity, and symmetry of analog RRAM devices and provided an impetus for the development of RRAM-based in-memory computing chips. The research results "Uniformity, Linearity, and Symmetry Enhancement in TiOₓ/MoS₂–xOₓ Based Analog RRAM via S-Vacancy Confined Nanofilament" have been published in the Nano Letters journal (Nano Lett. 24, 51, 16283–16292 (2024)).

Paper link: https://pubs.acs.org/doi/10.1021/acs.nanolett.4c04434

Atomic Abacus (ABACUS) is an open-source first-principles calculation software. It supports electronic structure calculations and molecular dynamics simulations based on density functional theory. ABACUS allows users to choose plane wave basis sets or numerical atomic orbital basis sets for functions such as electronic self-consistent iterations, band structure, density of states calculations, and structural optimizations. In addition, it supports multiple DFT theoretical methods including Kohn-Sham DFT, Real-time TDDFT, Stochastic DFT, Orbital-Free DFT. ABACUS supports various exchange-correlation functionals such as local density approximation (LDA), generalized gradient approximation (GGA), metaGGA, and hybrid functionals. ABACUS also provides good support for multiple AI-assisted algorithms such as DeePKS, DeePMD, DeePTB, and DeepH. The ABACUS development team aims to support high-precision, high-stability, multi-platform, and large-scale calculations. It conducts online collaborative development in an open-source mode and ensures the rapid iteration of ABACUS in terms of functionality, performance, and reliability by constructing a high-throughput and cross-platform automated test workflow.

Virtual screening is a crucial technique in the early stage of drug discovery, aiming to identify potential drug candidate molecules from vast molecular libraries. Ligand - based virtual screening, such as molecule search, has drawn significant attention as it does not rely on specific protein site information. Recently, researchers Zhen Wang, Feng Yu, Guolin Ke, and Zhifeng Gao from DP Technology, in collaboration with Professor Zhewei Wei from Renmin University of China and doctoral student Gengmo Zhou, published a paper titled "S - MolSearch: 3D Semi - supervised Contrastive Learning for Bioactive Molecule Search" at the top machine learning conference NeurIPS 2024. This paper introduced in detail a 3D semi - supervised learning framework called S - MolSearch for active molecule search. The S - MolSearch is designed based on the principle of inverse optimal transport and can effectively combine and utilize labeled and unlabeled data, demonstrating a remarkable improvement over existing virtual screening methods. Uni - Mol functions as a 3D molecule encoder and plays a central role in this process, showcasing its great potential in molecule representation and molecule similarity measurement and providing strong support for new drug discovery.

The research group led by David Rodney from the University of Lyon 1 collaborated with Fuzhi Dai (now a distinguished professor at the University of Science and Technology Beijing) from the Beijing Institute for Scientific Intelligence. They developed a high-precision deep neural network potential function model based on the DeePMD-kit software. This model reveals the complex phase-transformation-induced plasticity (TRIP) mechanism in ceria (CeO₂)-doped zirconia (ZrO₂) ceramics. The related results were published in the authoritative journal Acta Materialia in materials science under the title "Transformation-induced plasticity in CeO₂-ZrO₂ ceramics: Atomic-scale insights using a deep neural network potential" (https://doi.org/10.1016/j.actamat.2024.120661). The model and dataset have been made public on the AIS-Square platform for researchers to use: https://www.aissquare.com/models?search=CeO₂-ZrO₂. The first author of this paper is Jinyu Zhang (now a postdoctoral fellow at Osaka University), and the corresponding author is David Rodney.

With the increasing demand for molecular representation learning in drug discovery and materials design, multimodal representation learning that combines the 3D geometric structure of molecules with biomedical text is becoming a research hotspot. Recently, a paper titled "GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules" [1] published on arXiv introduced an innovative framework called GeomCLIP in detail. This framework combines the 3D geometric information of molecules with text descriptions and significantly enhances the multimodal learning ability of molecular representations through contrastive learning and denoising pre-training tasks, demonstrating superior performance in several downstream tasks.

The research team developed a large-scale dataset called PubChem3D, which contains more than 200,000 pairs of 3D geometry and text descriptions of molecules, covering rich chemical and biological information. The GeomCLIP framework adopts a dual-encoder structure to encode the geometry and text of molecules respectively and aligns the representations of the two modalities through contrastive learning while retaining the ability of the geometric encoder to model the characteristics of 3D molecules. Experiments show that GeomCLIP has achieved excellent performance in molecular property prediction, molecule-to-text retrieval, and 3D molecular caption generation tasks, providing important support for drug research and development and materials design.

This research was completed by a joint team from the Artificial Intelligence Research Laboratory at Pennsylvania State University and the Shenzhen International Graduate School of Tsinghua University. Teng Xiao from Pennsylvania State University is the first author and corresponding author of the paper, and the collaborators include Chao Cui, Huaisheng Zhu, and Professor Vasant G. Honava from Tsinghua University. This work has opened up a new direction for multimodal learning of molecular representations and also provided new solutions for drug discovery and materials science research.

Recently, the research group led by Zhiwei Li from the School of Physical Science and Technology at Lanzhou University and the research group led by Hanjie Guo from the Songshan Lake Materials Laboratory collaborated. A research paper titled "Short-range order and strong interplay between local and itinerant magnetism in GeFe₃N" published in "Physical Review B" used microscopic probing combined with DFT calculations (ABACUS+TB2J) to reveal the unique magnetic behavior of the GeFe₃N compound [1]. This compound belongs to the space group I4/mcm, has a complex crystal structure and unique microscopic magnetism, and has attracted extensive attention. This article will introduce the crystal structure, magnetic behavior, and the physical mechanism behind it of GeFe₃N based on this research paper. Master's student Tinghai Zhang is the first author, and Yantao Cao, Bo Zhang, and Liang Qiao provided experimental and data analysis support.

Water is not only one of the most familiar substances to humans but also a central figure in the long history of physical chemistry. The tetrahedral arrangement and network interactions between its molecules distinguish it from simple liquids.

For a long time, there has been no specific conclusion regarding whether there is a liquid - liquid critical point (LLCP) in water. Besides, researchers' understanding of water, especially when it acts as a solvent, is still incomplete.

To address the problem of technically and reasonably representing the thermodynamic and kinetic properties of water after the introduction of other chemical substances, a team from Seoul National University in South Korea proposed a scheme to examine the spatiotemporal characterization of water using a machine learning force field (MLFF) through deep potential molecular dynamics (DPMD).

This research, titled "Spatiotemporal characterization of water diffusion anomalies in saline solutions using machine learning force field", was published in "Science Advances" on December 11, 2024.

Currently, most water models are unable to fully capture the dynamic behavior of water after the addition of salt. Although classical force fields provide important insights, their simplifications and the omission of dynamic charge effects may distort our understanding of the real behavior of water.

The application advantages of MLFF in fields such as materials science and its processing speed, which is more than six orders of magnitude faster than first - principles methods in systems composed of several hundred atoms, make it stand out among all the options.