A Monte Carlo-Driven Genetic Algorithm and Digital Twin Approach for Quantitative X-ray Fluorescence Spectroscopy

Traditional X-ray fluorescence (XRF) quantification approaches often rely on spectral fitting and simplified attenuation models, which can introduce systematic errors for thick, heterogeneous, or geometry-dependent samples. Here we present a genetic algorithm (GA) framework, coupled with Monte Carlo (MC) radiation transport, that applies evolutionary optimization to both composition estimation and experimental parameter selection. Using Geant4 simulations as physics-accurate references, the GA iteratively optimizes elemental fractions and beam conditions based on spectral agreement. The composition GA consistently converges for simulated samples containing up to seven elements, outperforming random sampling and maintaining accuracy in low-information scenarios. An experiment-planning GA improves spectral quality by adjusting excitation energy, yielding more than an 80% increase in peak signal-to-noise ratio. Combined in a digital twin approach, the two stages reduce mean absolute error by 14.5% while refining experimental setup. Parallelization studies on high performance computing systems demonstrate that optimized multiprocessing and multithreading halve runtime, enabling scalability to larger problem sizes. This work demonstrates genetic algorithm-driven optimization with MC simulations as a powerful digital twin framework for XRF, enhancing both experimental planning and post-experimental analysis.