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.
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.
Recently, the Journal of Chemical Information and Modeling published a research work titled "DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials" [1]. DeePMD-GNN is a plugin for DeePMD-kit [2], which for the first time achieves seamless integration of external Graph Neural Network (GNN) potential energy models within the DeePMD-kit framework, including mainstream models such as NequIP and MACE.
Large atomic models (LAMs) have undergone remarkable progress recently, emerging as universal or fundamental representations of the potential energy surface defined by the first-principles calculations of atomic systems. However, our understanding of the extent to which these models achieve true universality, as well as their comparative performance across different models, remains limited. This gap is largely due to the lack of comprehensive benchmarks capable of evaluating the effectiveness of LAMs as approximations to the universal potential energy surface.
Conformation, which refers to the different atomic arrangements a molecule can adopt in three-dimensional space, is one of the core factors determining a molecule's physicochemical properties. However, in current mainstream molecular machine learning modeling practices, conformational information is often ignored or simplified. Most prediction tasks still rely on two-dimensional structure representations, and there is even inconsistency in using the most stable conformational form. The lack of conformational information has become a significant factor limiting model prediction accuracy and also exposes the shortcomings of existing molecular representation methods in handling conformation-sensitive tasks.
