Jul 2026· Journal of Chemical Physics· Vol 165 4· 0 citations· 36 references
Medicine
Abstract
Determining the glass transition temperature Tg in materials science in general and for amorphous polymer systems in particular is a delicate matter due to the uncertainty in the definition of Tg and the complexity of the glass transition phenomenon itself. Machine learning (ML) provides powerful approaches for analyzing complex, high-dimensional data to reveal hidden patterns. Recently, we have applied an unsupervised ML technique to identify Tg of a polymer melt of weakly semiflexible bead-spring chains using the time evolvement of pairwise internal distances between monomers along chains, as input features. Here, we investigate the change of individual internal chain relaxation as the polymer melt transforms from the liquid to glassy state. The average overall relaxation remains unchanged and displays the usual temperature dependence. However, for some individual pair distances, scattered throughout the sample, relaxation is significantly delayed, which serves as a robust indicator of approaching the glass transition. Moreover, these changes and the first principle components are highly correlated. This is an evidence that the ML technique indeed captures a significant indicator of the approach of the glass transition. Typical experiments, which average over the whole sample, cannot identify such features.
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
Glass transition (Tg) and melting (Tm) temperatures set the processing window, service range, and end-use performance of polymers, making their rapid prediction central to accelerated materials design. Experiments and simulations are accurate but costly, while classical structure–property models depend on hand-crafted descriptors; machine learning instead maps chemical structure to thermal transitions end to end. This review organizes machine-learning prediction of Tg and Tm around three pillars—molecular representation, model architecture, and interpretability—and foregrounds the features that separate polymer informatics from generic molecular machine learning: repeat-unit periodicity, chain-length-invariant encoding, copolymer sequence, and stereoregularity. We compare descriptors, fingerprints, graph neural networks, and coarse-grained schemes; traditional, deep, transfer, and multi-task models under data scarcity; and methods for quantifying predictive uncertainty and delimiting applicability domains. We treat Tg and Tm as physically distinct targets, since Tm additionally reflects crystal packing, hydrogen bonding, and chain symmetry. A recurring theme is label quality: calorimetric, dynamic-mechanical, and thermomechanical measurements define these transitions differently, so pooled datasets embed instrumental as well as chemical variance. We critically assess explainable artificial intelligence methods and the way accuracy is reported, arguing that headline metrics are not comparable across studies, and we examine how the first community-scale prediction challenge, chemistry-aware data splitting, and calibrated uncertainty can place reporting on a common footing. Finally, we connect representations, architectures, and interpretability to high-performance and sustainable polymer design, synthesizability-aware screening, and closed-loop discovery, and outline open challenges in data scarcity, domain transfer, and chemical-space extrapolation.
Unknown authors· Frontiers in Materials· 0 citations
Melt memory in semicrystalline polymers is the remarkable ability of polymer chains to retain structural information from a prior crystalline state after being heated above the melting temperature. This phenomenon can induce extraordinary self-nucleation and strongly influence crystallization kinetics and final material properties, yet its molecular origin remains unresolved. Here, using molecular dynamics simulations of linear polymer chains in which the strength of nonbonded interchain interactions is systematically tuned, we show that enhanced interchain attractions stabilize nanoscale regions of increased density and extended trans-planar conformations that persist in the melt, as revealed by analyzing the dynamics through a density-field approach. These residual ordered regions in the melt act as self-nuclei upon cooling, providing a molecular explanation for experimental observations of persistent melt memory in polar polymers. By varying a single chemically meaningful parameter, i.e., the strength of interchain attraction, our model bridges weakly interacting polyolefins and polymers with stronger dipolar or hydrogen-bonding interactions, establishing a direct link between molecular cohesion and memory retention. The results demonstrate that melt memory originates from the interaction-mediated survival of localized structural order in the melt rather than from a completely randomized chain state. These findings provide a molecular framework connecting the chemical structure, intermolecular forces, and macroscopic crystallization behavior, offering new principles for controlling polymer solidification and designing semicrystalline materials with tailored properties.
A. de Nicola, A. Müller, Dario Cavallo et al.· Journal of the American Chem...· 0 citations
Crystalline solids melt at well-defined material-specific temperatures Tm via first-order phase transitions, whereas glasses undergo continuous transformations from solid to molten states at glass transition temperatures Tg, resembling second-order transitions. Despite extensive study, the microscopic origin of this distinction remains unveiled. In this work, both melting and glass transition are described within a unified framework based on analysis of thermally activated breakings of chemical bonds, treated as elementary excitations of condensed matter, termed configurons. The increasing concentration of configurons leads to a percolation transition corresponding to loss of mechanical rigidity of an elastic solid whose atoms are connected via chemical bonds. Configurons are delocalized and mobile in crystals, enabling their condensation and consequent latent heat release, whereas in glasses they are localized (Anderson localization), suppressing condensation and yielding a continuous transition from solid to molten states. The proposed framework provides a unified physical interpretation of phase transitions.
We investigate the slowing down of dynamics in a glass-forming mixture interacting via an inverse-power-law (IPL) potential using a combination of theory and large-scale molecular dynamics simulations. We measure the static pair-correlation function, configurational entropy, inherent-structure energy, and structural relaxation time. We employ a theoretical framework to calculate the structural relaxation time $\tau_{\alpha}$, which is found to be in very good agreement with the simulation results. The theory identifies a local structural order which defines the cooperativity of the relaxation and brings forth a fluctuation induced parameter $\psi ( T )$ and a crossover temperature $T_a$ that characterize the density and temperature dependence of the glassy dynamics. Furthermore, we determine a crossover temperature using independent dynamical and thermodynamic criteria and compare with the theoretically predicted crossover temperature $T_a$. Relaxation dynamics is shown to obey density-temperature scaling, similar to thermodynamic properties, in terms of a variable $\Gamma$ formed by an appropriate combination of density and temperature, characteristic of IPL interactions. Finally, we show that, when the excess thermodynamic and dynamic quantities obtained at different densities are plotted as functions of the reduced temperature $T/T_a$ (or $T_a/T$), the data collapse onto master curves with excellent agreement between theory and simulation. These scaling relations provide a unified description of the thermodynamics and dynamics in IPL systems, enabling the prediction of relaxation behavior over a wide range of densities from data at a single state point.
Here we develop an elasticity-based theory of crystallization in glasses that incorporates structural heterogeneity, fictive temperature, and polymorph-mediated pathways. In a glass, structural degrees of freedom are effectively frozen, so that the fictive temperature Tf remains higher than the ambient temperature T, rendering the system intrinsically out of equilibrium. A central result is that the crystal-glass interfacial penalty is renormalized in fragile systems by soft, liquid-like regions, leading to a subquadratic mismatch energy scaling as ΣR3/2 rather than the classical R2 form. Applying this framework to ethanol, we show that nucleation proceeds preferentially via a two-step route through a plastic crystalline polymorph. The associated barriers are dramatically reduced: the glass-to-plastic step exhibits barriers of only ∼5 kBT, compared to ∼102 kBT for the direct glass-to-crystal transition. This large separation explains the dominance of the Ostwald pathway and the emergence of a pronounced time-temperature-transformation (TTT) nose. In contrast, silica retains the classical R2 scaling due to its rigid network, leading to very large barriers and suppressed bulk nucleation.
Biman Bagchi· ChemPhysChem· 0 citations
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