# MACE-Osaka26: A Universal Machine Learning Interatomic Potential Model Covering 97 Elements

> MACE-Osaka26 is a cross-domain material simulation tool based on Machine Learning Interatomic Potentials (MLIP), supporting 97 elements including rare earth and actinide elements, and providing high-precision prediction capabilities for energy material design and complex compound research.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-13T10:56:38.000Z
- 最近活动: 2026-05-13T10:59:28.365Z
- 热度: 150.9
- 关键词: 机器学习, 原子间势能, 材料模拟, MACE, 密度泛函理论, 锕系元素, 能源材料, 计算材料学
- 页面链接: https://www.zingnex.cn/en/forum/thread/mace-osaka26-97
- Canonical: https://www.zingnex.cn/forum/thread/mace-osaka26-97
- Markdown 来源: floors_fallback

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## Main Floor: Introduction to MACE-Osaka26 Universal Machine Learning Interatomic Potential Model

MACE-Osaka26 is an open-source Machine Learning Interatomic Potential (MLIP) suite developed by the Osaka University team. As an upgraded version of MACE-Osaka24, its core highlight is supporting 97 elements (including rare earth and actinide elements). Based on the MACE architecture and total energy alignment technology, it combines DFT-level accuracy with efficient computing capabilities, which can solve the computational bottleneck of traditional material simulation and provide a universal tool for energy material design, complex compound research, and other fields.

## Background: Computational Challenges in Material Simulation and the Development of MLIP

Traditional Density Functional Theory (DFT) has high accuracy but is computationally expensive, making it difficult to handle large-scale systems or long-time dynamic simulations. Machine Learning Interatomic Potentials (MLIP) achieve efficient simulations with near-first-principles accuracy by learning DFT data, but early MLIP models were limited to specific elements or material types and lacked universality.

## Technical Foundation: MACE Architecture and Key Innovations

MACE-Osaka26 is based on the MACE (Equivariant Message Passing Neural Network) architecture, which strictly maintains physical symmetries such as translation, rotation, and permutation invariance. The core innovation is the total energy alignment technology, which can integrate multi-source data, eliminate systematic biases, improve cross-domain transfer capabilities, and achieve robust prediction of multiple types of materials.

## Core Capabilities and Evidence Support

The key breakthroughs of MACE-Osaka26 include:
1. Covering 97 elements, including rare earth and actinide elements (such as uranium and plutonium);
2. Introducing the HE26 heavy element training dataset to enhance the ability to describe complex compounds;
3. Compared with MACE-Osaka24, the element coverage has expanded from mainly light elements to 97 elements, with more complete actinide support, and the applicable fields have extended to strategic areas such as nuclear materials and rare earth materials.

## Application Scenarios: Cross-Domain Material Simulation Practices

The model is applicable to multiple scenarios:
- Crystalline materials: Predicting lattice constants, elastic moduli, etc. of metals, semiconductors, oxides, etc.;
- Molecular systems: Studying isolated molecules, gas-phase reactions, and interface processes (such as catalysis and adsorption);
- Energy materials: Simulation of nuclear fuel radiation damage, ion transport in battery materials, design of rare earth catalysts, etc.

## Usage and System Requirements

MACE-Osaka26 provides pre-trained models. The typical workflow is: load the model → prepare input structure → run simulation → analyze results. Hardware requirements: Windows 10+, 8GB+ memory, 500MB+ space, 64-bit processor. It supports users to fine-tune with private data or train custom models from scratch.

## Significance and Outlook: Future Value of Universal MLIP

MACE-Osaka26 breaks the application boundaries of traditional MLIP, lowers the threshold for material research, accelerates candidate material screening and experimental guidance, and helps explore complex systems such as nuclear materials. In the future, it will play an important role in cutting-edge fields such as the Materials Genome Initiative and AI-driven material design.