DeepModeling

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

This document describes how to install ABACUS agent and Google ADK in a local Linux environment (including WSL) as of June 12, 2025, and how to run a simple ABACUS calculation after startup.

Requirements: conda and ABACUS (recommended to install v3.10 LTS version) are installed locally.

Background

ABACUS is a density functional theory (DFT) software initiated by the University of Science and Technology of China, and open-sourced and co-constructed by multiple domestic teams including Peking University, Institute of Physics of Chinese Academy of Sciences, Beijing Academy of Science and Intelligence, and Hefei Comprehensive Artificial Intelligence Research Institute. Since adopting the LGPL3.0 open-source license in 2021 and further embracing the open-source sharing concept together with the DeepModeling community, it has successively released 70 iterative versions. Both the software functions and ecosystem have been significantly developed thanks to the selfless contributions of the open-source community.

Recently, the research team of Professor Chen Xiumin from the National Engineering Research Center for Vacuum Metallurgy, Kunming University of Science and Technology, in collaboration with DeepSeek, has achieved research on the microscopic reaction mechanism of vanadium removal from crude titanium tetrachloride by aluminum addition through a new method of artificial intelligence-driven scientific research (AI for Science). This study utilized the Deep Potential Molecular Dynamics (DPMD) simulation method to efficiently explore the reaction mechanism of vanadium removal by aluminum addition at the nanosecond time scale and the spatial scale of tens of thousands of atoms. Theoretical simulation analysis and experimental research show that the vanadium removal reaction is a synergistic mechanism of reduction and complexation reactions. In the Al-Cl₂-TiCl₄-VOCl₃ system, the reduction process forms polynuclear complexes with aluminum, titanium, and vanadium as central atoms bridged by Cl and O atoms. These polynuclear complexes, catalyzed by AlCl₃, convert VOCl₃ into VOCl₂ and VCl₃ through the exchange and transfer of Cl and O atoms in two reaction pathways. In this study, DPMD provides a new means to understand specific reactions from a microscopic perspective. The study of this reaction mechanism not only helps with the recycling and utilization of vanadium resources but also provides a theoretical basis and innovative ideas for the optimization and improvement of vanadium removal reagents.

We are pleased to announce to the DeepModeling community that Uni-Mol Tools [1] is officially and independently released! As an important part of the Uni-Mol ecosystem, Uni-Mol Tools provides more flexible and efficient tool support for molecular AI research and applications with its characteristics of lightweight, out-of-the-box, and scenario-oriented.

On May 6, 2025, the DeepModeling Community released Community Manifesto 2.0, planning to rapidly expand exploratory work in the field of "AI literature reading" in the near future. Today, the SciAssess project has officially joined the DeepModeling Community. Developed jointly by DeepSeek and the Beijing Academy of Scientific Intelligence, this system is a testing benchmark specifically designed to evaluate the scientific literature analysis capabilities of large language models (LLMs), aiming to advance the process of AI empowering scientific research. SciAssess will collaborate with the community to launch explorations in the field of AI for literature analysis.

Water, one of the most common yet complex molecules, has long perplexed researchers with its thermodynamic properties. A controversial hypothesis suggests that under supercooled conditions, water may exhibit a "liquid-liquid transition (LLT)"—a transformation between low-density liquid (LDL) and high-density liquid (HDL)—governed by a "second critical point (LLCP)."

However, direct observation of the LLCP has been extremely challenging. Within experimentally accessible temperature-pressure ranges, liquid water readily freezes into ice, while simulations struggle to reach the microsecond timescale. As a result, this hypothesis has long remained an unresolved "mystery".

Recently, F. Sciortino et al. leveraged the DeePMD framework with the DNN@MB-pol potential model to conduct microsecond-scale molecular dynamics simulations, achieving high precision approaching CCSD(T) calculations. For the first time, they provided strong constraints on the location of the liquid-liquid phase transition critical point in water. Published in Nature Physics under the title "Constraints on the location of the liquid-liquid critical point in water," this work opens a new chapter in understanding water's anomalous behavior.

Recently, the Journal of Chemical Theory and Computation published a research work titled DeePMD - kit v3: A Multiple - Backend Framework for Machine Learning Potentials [1]. This work focuses on a core innovation in the DeePMD - kit v3 version - the multi - backend framework. The latest version has integrated four deep learning backends: TensorFlow, PyTorch, JAX, and PaddlePaddle.