DeepModeling

From the vivid colors of smartphone displays and the high efficiency of photovoltaic solar panels, to high–energy-density batteries and sharp bio-fluorescent imaging, organic optoelectronic molecules are indispensable. They serve as the “soul” and “modulator” of optoelectronic functions. With structural tunability at the molecular scale, they continuously enable the evolution of optoelectronic devices and their broad application scenarios.

However, to fully unlock the potential of organic optoelectronic materials, it is crucial to efficiently understand—across multiple scales—the intrinsic links between molecular structure, material properties, and device performance.

Recently, the Functional Molecular Design Team of AI for Science Institute (AISI), together with the DP Technology development team, in collaboration with Peking University, Sinopec Research Institute of Petroleum Processing, Shandong University, Henan Normal University, Shenzhen Institute of Synthetic Biology, and several other institutions, introduced OCNet—a pretraining framework for organic optoelectronic materials built upon the Uni-Mol architecture. OCNet is trained on tens of millions of conjugated molecules and their dimers.

OCNet achieves, for the first time, a unified virtual representation spanning molecules, mesoscale materials, and devices: it surpasses existing SOTA models by 20% on molecular-scale performance, enables cross-material generalizable mobility prediction in amorphous organic thin films for the first time, and delivers near-real-time, high-accuracy prediction of device-level photovoltaic efficiency. The work has been published in npj Computational Materials (doi: 10.1038/s41524-025-01788-y).

Hybrid functionals (HDFs) overcome the shortcomings of local/semi-local functionals—such as the underestimation of band gaps—by incorporating exact exchange (EXX), but this comes at the cost of high computational expense. ABACUS combined with LibRI enables linear-scaling calculations of hybrid functionals, and on this basis, applying space-group symmetry can further reduce the computational load.

Prior to version 3.8.0, ABACUS already supported symmetry acceleration for local/semi-local functionals: it reduces the number of Kohn-Sham (KS) equations to be solved by reducing k-points to the irreducible Brillouin zone (IBZ). However, due to the lack of implementation for space-group transformations of the density matrix, symmetry acceleration was not supported for cases involving non-local Hamiltonians (e.g., hybrid functionals). On the other hand, symmetry reduction can also be applied to real-space two-electron integrals (ERIs) for the EXX term. Nevertheless, currently available software (such as CRYSTAL and Turbomole) only implements this for algorithms that directly compute four-center integrals, without further accelerating symmetry application based on the resolution of the identity (RI) method—a common approach to speed up ERI calculations.

The official release of UniMol_Tools v0.15 introduces lightweight pre-training and a synchronized full-process command-line tool based on Hydra. Developers can complete the entire workflow from preprocessing → pre-training → fine-tuning → property prediction with just a few lines of code, and the reproduced results are nearly identical to those of the original Uni-Mol. This new version aims to provide an efficient and reproducible computing platform for research in materials science, medicinal chemistry, and molecular design.

In recent years, with the rapid advancement of mRNA vaccines and nucleic acid drugs, lipid nanoparticles (LNPs) have emerged as one of the most crucial drug delivery tools. However, the performance of LNPs depends on various lipid components and their proportions. Experimental optimization is not only time-consuming and labor-intensive but also struggles to cover the vast design space. Recently, the work led by Alvin Chan and his team was published in Nature Nanotechnology under the title "Designing lipid nanoparticles using a transformer-based neural network". The study proposes a transformer-based neural network model called COMET, which integrates molecular structures and formulation parameters to predict LNP performance. A key component of this process is the use of Uni-Mol as the core tool for molecular representation learning.

AI-driven drug discovery relies on the accurate characterization of molecular features. The newly launched MoleculeCLA dataset by Professor Lanyan Yan's team at the Institute for Intelligent Industry, Tsinghua University, provides a new, multi-dimensional evaluation platform for molecular representation by generating large-scale computationally docked chemical, physical, and biological properties with no experimental noise. On this benchmark, Uni-Mol performed exceptionally well in end-to-end fine-tuning, achieving an average Pearson correlation coefficient of 0.68, ranking first among all pre-trained deep models and traditional molecular descriptors. This fully demonstrates its advantages in molecular feature extraction and prediction tasks. The preprint of the research paper entitled "MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis" has been published on arXiv.

On July 21, 2025, a research paper by the Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences was published online in Phys. Rev. B, entitled "Soliton-like domain wall motion in sliding ferroelectrics with ultralow damping". The first authors of the paper are doctoral students Dr. Yubai Shi and Dr. Gaoxiang Yu. The corresponding authors are Associate Researcher He Ri and Professor Zhong Zhicheng. The paper was selected as Editors' Suggestion.

Niobium (Nb), a superconducting metal, plays an indispensable role in cutting-edge technological fields such as superconducting radio frequency cavities and nuclear fusion reactors due to its excellent superconducting properties, corrosion resistance, and high melting point. However, in these extreme service environments, niobium inevitably faces challenges from high-energy particle irradiation and microcrack propagation, which can severely damage its performance and even lead to catastrophic failure. Therefore, how to suppress irradiation damage and resist crack propagation is crucial to ensuring the safe and stable operation of related equipment.

DeepFlame is an open-source platform for combustion fluid computation developed in the era of AI for Science, aiming to promote the application of combustion fluid simulation technology in scientific research and industry [1–4]. Since its release, the platform has attracted extensive attention from academia and industry, and continues to attract outstanding developers and user communities to participate in its construction.

1. Preface

ABACUS has released the 3.10 - LTS stable version and is still being continuously iterated. Many users hope to deploy the ABACUS software on their own machines to experience the computing efficiency improvement brought by ABACUS. However, compiling ABACUS in different server and workstation environments and achieving the highest computing efficiency in these specific environments always presents some challenges.

The ABACUS Toolchain is a set of bash script collections built into the ABACUS repository. It can help users compile and install the software dependencies required by ABACUS online or offline, automatically handle the environment variables of each dependency library, and quickly complete the ABACUS source code compilation process based on these dependency libraries, realizing an efficient, high - performance, easy - to - modify, and easy - to - port automated ABACUS compilation solution.

This tutorial is written based on the ABACUS Toolchain of the 2025 - 02 version. At present, the ABACUS Toolchain supports the following compilation and installation functions:

• GNU Toolchain, that is, the Toolchain method of compiling and installing ABACUS dependency libraries and the ABACUS body from scratch starting from a sufficient version of the GNU compilation suite (gcc, g++, gfortran, collectively referred to as GCC).
• Intel Toolchain, that is, the Toolchain method of compiling and installing ABACUS dependency libraries and the ABACUS body based on Intel's compiler, mathematical library, and parallel library (usually packaged in Intel - OneAPI or Intel - parallel - xe - studio).
• AMD Toolchain, that is, the method of compiling and installing ABACUS based on AMD's compiler and mathematical library, which is subdivided into GCC - AOCL Toolchain and AOCC - AOCL Toolchain.

At the same time, the ABACUS Toolchain also supports a series of advanced functions including functional plug - in support and packaged offline installation.

In general, the vision that the ABACUS Toolchain hopes to achieve is:

• To facilitate users to efficiently compile the ABACUS most suitable for the current server environment from the source code, and to quickly test the computing efficiency of ABACUS compiled by different dependency library types of Toolchain.
• To establish a standard process for ABACUS source code compilation. ABACUS developers can directly control the version and compilation method of each ABACUS dependency library in the Toolchain without having to compile and manually add various compilation options by themselves.

There has been a previous tutorial introducing how to use the GNU Toolchain to simply and directly compile ABACUS from scratch: ABACUS Installation Tutorial - Toolchain (1 - GNU). This solution has the best compatibility, but the compiled ABACUS may not be the most efficient, especially for many Intel - CPU servers configured with the corresponding Intel OneAPI suite. This tutorial will focus on how to use the Intel Toolchain to make the compiled ABACUS obtain higher performance.

Recently, the Beijing Institute for Scientific Intelligence, in collaboration with the Shanghai Institute for Creative Intelligence, the Zhu Tong research group at East China Normal University, and New York University Shanghai, etc., pre-published the latest research progress in the field of large atom models on ChemRxiv under the title "General reactive machine learning potentials for CHON elements".

This study proposes a complete workflow for systematically constructing universal chemical reaction machine learning potential energy surfaces (MLPs) in the era of large atom models. It has breakthroughly built universal reactive MLPs for elements C, H, O, and N. Through innovative data construction and hybrid training strategies, it achieves chemical reaction simulation capabilities approaching DFT accuracy. The team proposed a dynamic sampling method of "wide coverage + active learning", generating the RXN-xTB pre-training dataset composed of over 17 million non-equilibrium structures and the fine-tuning dataset RXN-xTB-AL containing 200,000 structures. Combined with pre-training and Δ-learning collaborative optimization, the hybrid training strategy enables the DPA-3-DF model to achieve an MAE of 0.51 kcal/mol in energy prediction and 0.49 kcal/mol/Å in force prediction, significantly surpassing various existing mainstream neural network architectures. Dynamic simulation verification shows that the model can accurately characterize the dynamic bond fission process of complex reactions, providing a new paradigm that balances quantum accuracy and molecular dynamics efficiency for catalytic design and reaction mechanism analysis. This research achievement marks a major leap in machine learning potential energy in the field of chemical reaction modeling, providing a feasible new path for the precise and efficient simulation of typical organic reactions and catalytic systems.

Paper link:
https://chemrxiv.org/engage/chemrxiv/article-details/684ffe583ba0887c33dad39b