Unfolding an Atomistic World: Atomistic Simulation of Reactor Pressure Vessel Steel Across Year-and-Meter Scales

2026-04-27

AI Summary

This study addresses the multiscale challenge in predicting the lifetime of reactor pressure vessel steels, which spans from angstroms/picoseconds to meters/decades and is poorly bridged by traditional empirical models that fail to connect microscopic mechanisms with macroscopic degradation. To overcome this, the authors propose AtomWorld, a novel “atomic world” modeling paradigm that reframes kinetic Monte Carlo as a consequence-aware state transition model. By integrating first-principles energy landscapes, a physics-guided voxel-parallel scheme, and a low-synchronization, high-efficiency HPC pipeline, AtomWorld achieves full-atomistic simulations at meter-scale spatial and annual temporal resolutions for the first time. The framework successfully simulates systems of 10¹⁹ atoms, requiring only 1.71 days per simulated service year, and demonstrates strong scaling efficiencies of 92–97% across five leading supercomputers, reaching a peak performance of 1.27 EFLOP/s.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Lifetime prediction of reactor pressure vessel (RPV) steel requires bridging atomistic degradation mechanisms with service-scale spatial and temporal regimes, from Angstroms and picoseconds to meters and decades. Existing engineering-scale models provide long-range reach but rely on fitted degradation laws, while recent atomistic kinetic Monte Carlo (AKMC) advances still fail to achieve year-and-meter-scale coverage. We present AtomWorld, an atomistic world-modeling framework for RPV steel lifetime simulation co-designed with leadership-scale supercomputing through three tightly coupled layers: (1) algorithm: AtomWorld recasts classical AKMC as an atomistic world model that learns consequence-aware state transitions over the ab initio energy landscape; (2) HPC: it co-designs this formulation with modern supercomputers, yielding a compute-dense, synchronization-light, and communication-efficient execution pipeline; and (3) application: it extends atomistic world modeling to engineering-scale simulation through a physically grounded voxel-parallel framework, offering a scalable pathway from local atomistic dynamics to engineering-scale degradation evolution. We demonstrate a paradigm shift in atomistic simulation: AtomWorld enables atomistic simulation of RPV steel across year-and-meter scales for the first time, extending direct atomistic modeling to ten-quintillion-atom systems and achieving a time-to-solution of 1.71 days for one simulated service year. These capabilities are sustained across five leadership supercomputers with 92-97% scaling efficiency and peak performance up to 1.27 EFLOP/s, corresponding to 48% of the Lineshine peak FP64 performance.

Problem

• reactor pressure vessel steel
• lifetime prediction
• atomistic simulation
• multi-scale modeling
• degradation mechanisms

Innovation

• atomistic world model
• kinetic Monte Carlo
• exascale computing
• voxel-parallel simulation
• reactor pressure vessel steel