Year: 2026
Type: preprint
Abstract: Realistic surfaces of active materials that involve the motion of atoms or ions in their function typically change their (sub-)surface composition and geometry under operating conditions due to varying environmental stimuli, degradation or healing processes, but also just while exhibiting their intended function, making it difficult to build simulation models that remain valid as the system evolves. Such problems often demand large-scale computation together with efficient data processing, so scalable task execution and data-efficient synchronization become critical. We present a scalable computational framework that supports large collections of independent computation tasks with incremental result aggregation. We demonstrate the framework using adaptive kinetic Monte Carlo (akMC) as a showcase application, where transition-state searches are performed on-the-fly. The new framework employs a hierarchical orchestrator–worker architecture implemented with modern MPI constructs and operates efficiently from single workstations to multi-node high-performance computing systems. It integrates well-established simulation engines such as LAMMPS and supports multi-core CPU and GPU parallelism within each task. Performance is demonstrated using classical, machine-learning, and GPU-accelerated interatomic potentials. Benchmarks show strong scaling across multiple compute nodes with several hundreds of CPU-cores, efficient utilization of multi-core executors and GPU sharing. The results indicate that the framework provides an efficient solution for rare-event and mesoscopic simulation workflows.
Source: ChemRxiv
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7213265202