Baicheng Mei

This work develops a data-driven framework for predicting the thermal conductivity of metals and multi-component alloys and for inversely proposing compositions that meet a target conductivity. We collect, to our knowledge, the largest experimental dataset containing 6259 data points spanning 49 elements and temperatures from 0 to 1400 K. Using alloy composition and temperature as inputs, we train and benchmark several regression models and obtain high predictive accuracy reaching R² > 0.99 and RMSE of 6–9 W m⁻¹ K⁻¹. The approach remains quantitatively reliable for challenging cases including dilute-doped Mg alloys and commercial steel over broad temperature ranges. Based on the trained forward model, we propose an inverse-design workflow to efficiently search composition space and suggest candidate alloys expected to achieve a specified thermal-conductivity target at a given temperature. The inverse search can identify composition windows where near-target conductivity is maintained over a finite concentration range to improve the practical ability for experimental validation and scalable process.

The slow dynamics of non-concatenated ring melts remains a frontier problem in polymer science with implications for many soft material environments including cellular biophysics. Here, we report large-scale simulations of model ring melts that analyze the monomer and center-of-mass (CM) mean square displacements (MSD) and stress relaxation function on intermediate time and length scales. The degree of dynamical slowing down is characterized by the maximally sub-diffusive fractional time scaling exponents. The data span an exceptionally wide range of ring degrees of polymerization and stiffnesses and are not successfully organized based on the classic measure linear chain entanglement, N/Ne. Rather, we find that the crossover degree of polymerization, ND, based on ring macromolecular caging that successfully allows master curves to be constructed for the long-time CM self-diffusion constant also collapses these temporal dynamic scaling exponents. Different properties display different exponents and exhibit one or two regimes of linear variation with the logarithm of ND/N. A distinct crossover of the CM-MSD and stress relaxation exponents emerges at sufficiently large N or stiffness that is not found for the monomer MSD, indicating a novel form of dynamic decoupling. This crossover aligns with the predicted critical degree of polymerization for transitioning from a weak to strong caging regime, indicative of activated transport. The latter may reflect the emergence of an intermolecular collective contribution to stress in analogy with dense soft colloidal matter. Suggestions are made for future theoretical work to address the rich patterns of behavior discovered.

This review highlights the innovative design principles and applications of low- T _g polymer-based flexible RTP materials, paving the way for next-generation soft optoelectronics.

This study developed an efficient color-tunable afterglow material through a co-doping and triplet-to-singlet Förster resonance energy transfer strategy, enabling applications in anti-counterfeiting and data encryption.

Understanding and predicting the glassy dynamics of polymers remain fundamental challenges in soft matter physics. While the elastically collective nonlinear Langevin equation theory has been successful in describing relaxation dynamics, its practical application to polymers depends on thermal mapping, to connect the theory with the experiment, which, in turn, requires detailed thermodynamic data. Such data may not be available for chemically complex or newly designed polymers. In this work, we propose a simple approach that integrates machine learning-predicted glass transition temperatures (Tg) with a simplified thermal mapping based on an effective thermal expansion coefficient to overcome these limitations. This approach can provide quantitatively accurate predictions of relaxation dynamics across a broad range of polymers. Rather than replacing the original thermal mapping, our method complements it by trading formal rigor for computational efficiency and broader applicability in high-throughput screening and materials with limited available data. Moreover, we introduce a physically motivated modification to the thermal mapping that resolves discrepancies in the description of low-Tg polymers. Our results establish a generalizable approach for predictive modeling of glassy polymer dynamics and point toward new directions for theory-guided materials discovery.

A successful microscopic theory of activated relaxation in metastable supercooled liquids is extended to the equilibrated deep glass regime. Surprisingly, the predicted power-law scaling connections of the dynamic barrier with diverse scalar order parameters (medium-range order correlation length, dimensionless compressibility, shear modulus) remain unchanged up to astronomically long timescales, despite a fundamental crossover of equilibrium thermodynamics and structure near the laboratory kinetic vitrification point. Quantitative tests against experiments on aged to equilibrium glass-forming liquids up to nearly 20 decades in time scale reveal good agreement. This conflicts with the idea of a crossover from super-Arrhenius to literal Arrhenius relaxation around the laboratory glass transition temperature, and supports the robustness of the theoretical idea that ultraslow dynamics is causally related to medium-range structural order. New avenues of experimental and theoretical research in the deep glass regime are suggested.

The HF crossover frequency, obtained from rheology (a), reflecting the extent of in-cage particle motion, exhibits two exponential regimes in accord with (b) non-linear Langevin theory with modified Verlet approximation predictions.