DeepModeling

A research group led by Prof. Xu Shenzhen from the School of Materials Science and Engineering, Peking University, published the paper Electrochemical Potential Fluctuation Matters in Rate Constant Calculations for Proton-Coupled Electron Transfer in Journal of Chemical Theory and Computation. The study systematically compared two mainstream constant-potential molecular dynamics (MD) methods for predicting electrochemical reaction rates. Taking the Volmer step of the hydrogen evolution reaction (HER) on Pt(111) as the model system and adopting the Bennett-Chandler method, the team found that the rate constants calculated by the two approaches differ by approximately four times. The rigorous method allowing natural work function fluctuations (CPwfluc.) yields a rate constant around four times higher than the method fixing instantaneous work functions via electron number iteration (CPw/ofluc.).

Researchers led by Dr. He Yu from the Institute of Geochemistry, Chinese Academy of Sciences, and Associate Professor Zhang Wei from Guizhou Normal University, together with international collaborators, published the paper Absence of dehydration due to superionic transition at Earth’s core-mantle boundary in Science Advances. Combining the domestic ab initio molecular dynamics software ABACUS and Deep Potential Molecular Dynamics (DPMD), the team investigated the thermodynamic stability of water-bearing minerals and water under conditions of Earth’s lower mantle, especially the core-mantle boundary (CMB). The results show that water and the key hydrous mineral δ-AlOOH transform into a special superionic state under extreme high pressure and temperature, which strongly suppresses dehydration.

Over the past two years, the DeepFlame community has witnessed the rapid development of AI for Science (AI4S) together with researchers and practitioners. Since advocating the co-construction of an AI4S open-source combustion platform in June 2022, and releasing more than twenty versions that realize full-process GPU heterogeneous solvers, we have consistently been committed to building a bridge between artificial intelligence, high-performance computing, and physical modeling.

However, in today’s era of explosive AI growth, why are many researchers’ daily routines still dominated by heavy code debugging and case configuration? True AI4S should not stop at “using AI to compute faster,” but should aim to “use AI to liberate researchers’ productivity.”

Today, we officially release DeepFlame 2.0. In this version, beyond functional updates and performance optimizations, more importantly, we formally introduce a brand-new scientific computing paradigm — AI-agent-driven scientific computing. By bringing AI agents into scientific computing workflows, researchers can leverage the power of intelligent agents to improve research efficiency and focus more on solving scientific problems themselves.

Given a chemical formula, for example Cu₁₂Sb₄S₁₃, how should the atoms be arranged in space in order to form a stable crystal? This is the problem of crystal structure prediction (Crystal Structure Prediction, CSP), one of the fundamental challenges in materials science research. Recently, the Institute of Physics, Chinese Academy of Sciences released CrystalFormer-CSP to the DeepModeling community, adopting a strategy that combines “fast thinking” and “slow thinking” to address this challenge.

In the era of AI for Science, where research workflows are being fundamentally reshaped, the ability to acquire high-throughput, high-quality data has become the key competitive edge. Yet today’s labs still face major challenges: heterogeneous hardware, closed protocols, and fragmented data flow force researchers to spend precious time on integration overhead—not scientific discovery.

An automated lab should not be a collection of expensive machines, but an extension of intelligent decision-making.

In December 2025, with the official merge of v0.10.13, Uni-Lab-OS enters version 1.0. We aim to provide a standardized digital infrastructure for the scientific community—breaking down device barriers and freeing innovation from tooling constraints.

You may not know this: a silicon wafer that looks perfectly smooth is, at the atomic scale, actually a dynamic stage—silicon atoms pair up into dimers, hydrogen atoms shuttle back and forth, and at high temperatures the surface even “pre-melts,” forming a quasi-liquid layer that resembles sweating. These microscopic behaviors directly affect the quality of chip manufacturing.

Recently, the research team led by Professor Li Pai and Professor Wei Xing from the Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, published a study in Small. For the first time, they systematically revealed how the Si(001) surface evolves under different temperatures and hydrogen environments, and captured its pre-melting phenomenon before bulk melting occurs. All of this relies on a key tool: Deep Potential (DP).

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.