RNMC: kinetic Monte Carlo implementations for complex reaction networks

2024/12/17 by Laura Zichi, Daniel Barter, Eric Sivonxay +5 · 1 voice · 1 citation
Chemical Engineering · Engineering · Materials Science · #Advanced Memory and Neural Computing #Catalysis and Oxidation Reactions #Machine Learning in Materials Science

paper · pdf · doi:10.21105/joss.07244

openalex publication_date 2024/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

Macroscopic chemical and physical phenomena are driven by microscopic interactions at the atomic and molecular scales.In order to capture complex processes with high fidelity, simulation methods that bridge disparate time and length scales are needed.While techniques like molecular dynamics and ab initio simulations capture dynamics and reactivity at high resolution, they cannot be used beyond relatively small length (hundreds to thousands of atoms) and time scales (picoseconds to microseconds).Kinetic Monte Carlo (kMC) approaches overcome these limitations to bridge length and time scales across several orders of magnitude while retaining relevant microscopic resolution, making it a powerful and flexible tool.

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• Sub-10 nm upconversion nanocrystals for long-term single-particle tracking

Discussions

• Check out NPMC, a kinetic Monte Carlo package for predicting optical spectra of lanthanide-doped materials like the complex core/shell nanoparticles we synthesize. NPMC is part of RNMC, a kMC impleme [bsky, 5 points, 1 comments]

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