DeepModeling

Phase diagrams and thermodynamic properties of deep Earth materials are fundamental to geophysics, geodynamics and geological research. As the core chemical system of mantle mineralogy, the Mg-Al-Si-O system governs the stability and physical properties of dominant mantle minerals and melts, further modulating mantle dynamics and plate tectonics. Recently, Xin Zhong, Timm John from Free University of Berlin and Yifan Li from Princeton University published their research A general purposed machine learning interatomic potential for Mg-Al-Si-O system suitable for Earth materials at high pressure and temperature conditions in npj Computational Materials. The team developed a universal machine learning interatomic potential for the Mg-Al-Si-O system. By combining the r2SCAN functional with pairwise Gaussian energy correction, the average enthalpy error of over 20 mineral phases was reduced from 5.2 kJ/mol to 1.2 kJ/mol. The potential reproduces phase diagrams of systems including SiO₂ Al₂SiO₅ and Mg₂SiO₄ with excellent consistency against experimental measurements. The study quantitatively characterizes the anisotropy of solid–melt interfacial free energy for periclase and forsterite at the atomic scale, and quantifies how non-hydrostatic stress modulates the α-β quartz phase transition.

Next-generation nuclear fission and fusion reactors impose extremely stringent requirements on structural materials, which must simultaneously withstand high temperatures, high-dose irradiation, and strongly corrosive coolants. Multi-principal element alloys (MPEAs) are regarded as highly promising candidate materials owing to their unique high-entropy effect, lattice distortion, sluggish diffusion, and cocktail effect. Nevertheless, understanding irradiation damage and mechanical behaviors at the atomic scale in these complex alloys demands interatomic potentials (IAPs) with high precision and universal transferability. Although conventional machine learning interatomic potentials (MLIAPs) achieve decent accuracy, the volume of training datasets rises exponentially for quinary or higher-order complex systems, leading to prohibitive computational costs for generating DFT reference labels.

Recently, the OpenLAM Team of the AI fof Science Institute,Beijing(AISI), Peking University, DP Technology, and the Institute of Applied Physics and Computational Mathematics have jointly launched DPA4, a new-generation model architecture tailored for the era of Large Atomic Models (LAMs). DPA4 claimed the top spot worldwide with its comprehensive performance score (CPS) on Matbench Discovery, an authoritative global benchmark for materials discovery, emerging as the latest State-of-the-Art (SOTA) model.

DPA4’s highlight lies in its ultra-low training threshold: the prior leading eSEN needed over 300 GPU days for training, yet DPA4 reaches matching accuracy with merely one consumer RTX 5090 running for roughly one day, and its parameter volume is less than one-tenth of eSEN’s.

In short, the SOTA-level accuracy once reliant on costly supercomputing is now accessible via a single consumer graphics card. DPA4 reshapes the accuracy-efficiency Pareto frontier of large atomic models.

Official Screenshot of Matbench Discovery (Data as of May 22, 2026)

A research team led by Dr. Xue Hongtao and Dr. Chang Zhen from State Key Laboratory of Advanced Processing and Recycling of Non-ferrous Metals & Gansu Center for Materials Genome and Fundamental Structural Research, Lanzhou University of Technology, has published new findings in Journal of Materials Research and Technology. The paper titled Deep potential driven molecular dynamics simulations on tensile properties of nano-grained Al–Ce alloys: Mechanisms of Ce segregation and strengthening develops a high-precision deep potential (DP) for Al-Ce alloys and clarifies the microscale strengthening mechanism induced by grain boundary segregation of trace rare earth cerium (Ce) in nanocrystalline aluminum alloys.

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).