Accurate description of structural relaxation processes is essential for predicting long-term stability of chalcogenide glasses utilized in memory, photonic, and infrared-optical applications. Although the Tool–Narayanaswamy–Moynihan (TNM) model remains one of the most effective frameworks for describing the glass transition kinetics, determination of its nonlinearity (x) and nonexponentiality (β) related features is often hindered by instrumental distortions and instability of the conventional curve-fitting procedures. Here, a robust and user-friendly implementation of the simulation-comparative method for extraction of the x and β TNM parameters is presented. An extensive library of precomputed core data sets is introduced, covering physically relevant ranges of enthalpy relaxation activation energy (200–500 kJ·mol–1) and glass transition temperatures (−50–450 °C) for chalcogenide glasses. Utilization of these data sets in terms of the presented simulation-comparative method enables rapid, fitting-free estimation of x and β by direct comparison with experimental calorimetric data, using only basic data-processing tools. The predictive performance of the method was systematically validatedusing defined and randomly generated theoretically simulated data sets, as well as experimental calorimetric data extracted from the literaturethe method predicts the parameters x and β with an uncertainty of ±0.05, further decreasing this uncertainty to ±0.02 for the most relevant cases of the relaxation behavior. By eliminating the need for specialized software and unstable optimization routines, the presented approach substantially lowers the barrier for routine TNM analysis and enables reliable monitoring of subtle structural relaxation trends in chalcogenide glasses. The provided core data sets, together with accompanying MATLAB code, establish a practical and broadly applicable platform for structural relaxation studies in amorphous chalcogenide materials.
The glass transition temperature (Tg) is a critical descriptor governing the morphological stability, emitter orientation, and interfacial integrity of amorphous thin films in organic electronics. However, experimental Tg measurements suffer from high resource costs and interlaboratory variability, while machine learning models are bottlenecked by scarce, noisy data sets. Here, we establish a physics-based atomistic molecular dynamics (MD) protocol to predict the Tg of 160 diverse organic electronic materials. To study computational throughput and predictive accuracy, we systematically benchmarked nine configurations spanning system sizes (5,000, 10,000, and 15,000 atoms) and cooling step relaxation times (5, 10, and 15 ns). Extracted via an automated, bias-free hyperbolic fitting scheme, our preferred standalone workflow (15,000 atoms, 15 ns) yields a correlation of R2 = 0.89 and a mean absolute error (MAE) of 10.9 K relative to experiment. Structural descriptor analysis confirms that accuracy remains uniform regardless of molecular weight or heteroatom density, establishing this transferable workflow as a digital sieve to accelerate the discovery of next-generation organic electronics.
Hadi Abroshan, Paul Winget, H. Kwak et al.· Journal of Physical Chemistr...· 0 citations
The dynamic behaviour of quadrol (N,N,N',N'-tetrakis(2-hydroxypropyl)ethylenediamine) in its glassy and supercooled liquid states is examined through a combined experimental and computational approach. Owing to its strong hydrogen-bonding capability and stereochemical complexity, quadrol avoids crystallization and exhibits a glass transition close to -30 °C. Dielectric relaxation spectroscopy (DRS), thermally stimulated depolarization currents (TSDC) and differential scanning calorimetry (DSC) have been employed to probe the α-relaxation, secondary relaxation modes and physical aging phenomena. The α-relaxation shows a marked non-Arrhenius temperature dependence and dominates the dielectric response, either in DRS and TSDC results; additionally, calorimetric and TSDC measurements reveal a strong sensitivity to thermal history and aging conditions. Below Tg, two secondary relaxations become clearly discernible. The faster γ-relaxation, which appears at lowest temperatures, exhibits kinetic features characteristic of localized molecular motions. By contrast, the slower β-relaxation shows a pronounced sensitivity to both aging and water content, signaling a more complex underlying mechanism. The dc electrical conductivity follows a VFTH-like temperature dependence and displays a partially decoupled from the α relaxation. Complementary molecular simulations provide atomistic insight into conformational preferences and hydrogen-bonding environments, offering a microscopic basis for the observed relaxation behaviour. Taken together, these results reinforce the value of quadrol as a model system for probing the interplay between molecular mobility, hydrogen bonding, and dynamic arrest in amorphous organic materials.
M. T. Viciosa, Carlos E. S. Bernardes, Andreia F. M. Santos et al.· Physical Chemistry, Chemical...· 0 citations
The glass transition in rapidly quenched Li17Pb83 remains a fundamental puzzle, particularly regarding the coupling among structural, dynamic and electronic properties in binary asymmetric alloy systems. Here, we perform ab initio molecular dynamics simulations during quenching to characterize its multi-scale evolution. A kinetic glass transition temperature Tking = 386.0 K is determined from the two-stage Arrhenius dependence of Li diffusion. Structural analysis reveals progressive enhancement of Pb short/medium-range order upon cooling, with the Pb–Pb coordination number increasing from 5.09 to 5.33 and the formation of Pb cage-like structures. Concurrently, Li dynamics transition from long-range hopping to localized rattling motions within these cages, as evidenced by van Hove correlation functions and phonon density of states. Electronic structure evolves synchronously. Pb-6p-dominated electron localization intensifies within the Pb cage network, accompanied by slight charge redistribution (Li: +0.136e to +0.130e, Pb −0.028e to −0.027e) and a marginal decrease in Pb–Pb bond order. These electronic changes are intimately linked to structural ordering and dynamic slowdown, providing atomic-scale evidence for the correlated nature of the glass transition. We propose a synergistic structure-dynamics-electron mechanism, Pb cage densification as the structural foundation, Li mobility arrest as the dynamic signature, and Pb-6p localization as the electronic feature that collectively accompanies the dynamic freezing of interstitial Li atoms. This work complements classical glass transition theories with atomic-scale electronic evidence and offers a multi-dimensional characterization approach for asymmetric binary amorphous alloys.
Sensen Lin, Yang Gao, Yongheng Lu et al.· RSC Advances· 0 citations
Chemical short-range order (CSRO), the non-random local arrangement of atoms in solid solutions, strongly affects the phase stability and performance of medium- and high-entropy alloys (M/HEAs). Despite its importance, the fundamental nature of CSRO formation remains contested: is it a formal thermodynamic transition? Here, investigating CoCrNi as a model system, we indicate that the main CSRO transformations observed in alloys might not be classical thermodynamic transitions, but instead a kinetic arrest phenomenon analogous to the glass transition. Combining atomistic simulations and in situ synchrotron dilatometry experiments enabled the study of CSRO evolution and its structural impact across multiple length scales. For example, CSRO-driven changes in bond lengths and bond distribution significantly impact the observed lattice parameter and volume, allowing Warren-Cowley parameter values to be determined over a full experimental temperature range. We demonstrate that the degree of CSRO and the apparent transition temperatures, defined here as the komplex reaction temperatures (Tkr), are not intrinsic material constants. Rather, they are path-dependent quantities governed by thermal history and diffusional constraints, directly reflecting the frozen CSRO state. Our findings clarify the thermodynamic and kinetic mechanisms underlying CSRO evolution and establish a framework to distinguish thermodynamic transitions under kinetic constraints from genuine kinetically arrested phenomena. Understanding this distinction is crucial for controlling CSRO during alloy design and processing and provides a foundation for future investigations exploring the implications of CSRO in advanced materials. In situ synchrotron dilatometry and atomistic simulations indicate that changes between chemical short-range order states (evolving local chemical distributions) are governed by a kinetic arrest phenomenon rather than a thermodynamic transition.
G. C. Stumpf, Yifan Cao, V. Bacurau et al.· Nature Communications· 0 citations
The glass transition temperature (
T
g
) and underlying segmental dynamics are fundamental parameters governing the thermomechanical and macroscopic properties of polymeric materials. Honoring the 65th birthday of Professor Sindee L. Simon, this perspective first highlights several of her pioneering contributions to the understanding of structural recovery and nanoconfinement in polymers, using Flash differential scanning calorimetry (Flash DSC). While physical confinement heavily dictates the behavior of traditional linear polymers, semirigid conjugated polymers, the cornerstone of modern flexible electronics, experience an intrinsic “self‐confinement” arising from nanoscopic domains between their highly rigid backbones and packed, flexible side chains. This unique architecture yields extreme dynamic heterogeneity and vanishingly small heat capacity changes (Δ
C
p
), rendering traditional DSC largely ineffective. Inspired by Professor Simon's methodologies, this perspective will describe how the application of Flash DSC to conjugated polymers successfully overcomes these kinetic and thermodynamic barriers, isolating elusive thermal transitions. We explain how understanding and manipulating polymer dynamics directly impacts macroscopic device engineering: from maintaining viscoelastic mechanical compliance in soft wearable electronics to engineering stable organic photovoltaics. Finally, we highlight the necessity of a multi‐modal approach, coupling Flash DSC with the microscopic structural resolution of neutron scattering to fully map the evolving dynamic landscape of conjugated polymers.
Metallic glasses challenge the classical structure–property doctrine because the lack of long-range periodicity makes their structures complex and inherently multiscale. Although short-range order (SRO) within the first-neighbor cage has provided a useful vocabulary (e.g., icosahedral versus liquid-like motifs), growing evidence shows that SRO alone is not a reliable state variable for properties, such as stability and mechanical performance. In particular, the same nominal SRO motif can correspond to distinct energetic states depending on its embedding at more extended length scale, and local propensity indicators do not uniquely predict whether local plastic activity remains isolated or evolves into avalanches and extensive plastic deformation. We argue that rethinking glass structure requires shifting from per-atom SRO coordinates toward collective variables, defined over the range of relevant cooperativity. Within this framework, we clarify two kinds of medium-range order (MRO) that are frequently conflated in the literature at the intermediate scales (around second nearest- neighbor shell, 0.5–1 nm). One is the topological MRO, manifested as SRO motif connectivity/networking in atomistic models or as crystal-like nanodomains, probed by fluctuation electron microscopy and 4D-STEM. Another is defined by the persistent oscillations of pair distribution functions (i.e., the density wave fluctuations). Their coherence length is also about the same scale, and provides an alternative, more general view of the MRO. This motivates an energy landscape-based viewpoint in which density-fluctuation fields do not map to single basins, but condition the statistical accessibility of basins and activation barriers, thereby shaping the rigidity network that steers cascade propagation. We conclude by outlining quantitative opportunities to connect these scale-dependent collective descriptors, response-based diffraction, and energy landscape sampling into predictive, experimentally anchored structure–property relations. Data-driven atomistic analysis of metallic-glass structures using smooth overlap of atomic positions (SOAP) descriptors and transparent machine learning to identify radius of informative structural environments (RISE) Data-driven atomistic analysis of metallic-glass structures using smooth overlap of atomic positions (SOAP) descriptors and transparent machine learning to identify radius of informative structural environments (RISE)