Resolving energy transfer dynamics in Eu²⁺-activated multi-site phosphors via metaheuristic optimization and physics-informed neural networks
Year: 2026
Type: article
Abstract: Multi-exponential fitting has long been the standard approach for luminescence decay analysis, not because it is physically meaningful but because it offers empirical convenience. Rigorous nonlinear rate-equation models have existed for decades, yet their application was hindered by computational costs. Recent advances in artificial intelligence and high-performance computing now make such models tractable, enabling physically grounded analyses of complex relaxation dynamics. Here we examine donor–acceptor interactions in a prototypical Eu2+-activated multi-site phosphor (La2.544Ca1.456Si12O4.456N16.544:Eu2+) that exhibits wavelength quenching. Metaheuristic-driven Runge–Kutta simulations enabled the extraction of quantitative radiative and non-radiative rate constants, while physics-informed neural networks provided a complementary framework that independently reproduced the experimental decay dynamics. Both approaches converged to consistent rate constants, establishing donor–acceptor transfer as the dominant relaxation pathway over radiative and same-species interactions, offering quantitative, physics-based insight into complex dynamical behaviors beyond phosphors. Multi-exponential fitting is commonly used for photoluminescence (PL) decay despite its limited physical meaning, largely replacing computationally demanding rate-equation models. The authors use AI-assisted simulations and a physics-informed end-to-end framework to fit PL decay data, obtaining results consistent across both approaches.
Source: Nature Communications
Institutions: Sejong University, Sunchon National University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,990
OpenAlex ID: W7124828740