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Recalcitrance in microplastics accounts for ubiquitous white pollution. Of special interest are the capabilities of microorganisms to accelerate their degradation sustainably. Compared to the well-studied pure cultures in degrading natural polymers, the algal-bacterial symbiotic system is considered as a promising candidate for microplastics removal, cascading bottom-up impacts on ecosystem-scale processes. This study selected and enriched the algae-associated microbial communities hosted by the indigenous isolation Desmodesmus sp. in wastewater treatment plants with micro-polyvinyl chloride, polyethylene terephthalate, polyethylene, and polystyrene contamination. Results elaborated that multiple settled and specific affiliates were recruited by the uniform algae protagonist from the biosphere under manifold microplastic stress. Alteration of distinct chemical functionalities and deformation of polymers provide direct evidence of degradation in phycosphere under illumination. Microplastic-induced phycosphere-derived DOM created spatial gradients of aromatic protein, fulvic and humic acid-like and tryptophan components to expanded niche-width. Surface thermodynamic analysis was conducted to simulate the reciprocal and reversible interaction on algal-bacterial and phycosphere-microplastic interface, revealing the enhancement of transition to stable and irreversible aggregation for functional microbiota colonization and microplastics capture. Furthermore, pangenomic analysis disclosed the genes related to the chemotaxis and the proposed microplastics biodegradation pathway in enriched algal-bacterial microbiome, orchestrating the evidence for common synthetic polymer particles and ultimately to confirm the effectiveness and potential. The present study emphasizes the necessity for future endeavors aimed at fully leveraging the potential of algal-bacterial mutualistic systems within sustainable bioremediation strategies targeting the eradication of microplastic waste. 

The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces. While their capability for modeling phonon properties is emerging, systematic benchmarking across chemically diverse systems remains limited. We evaluate six recent uMLPs—EquiformerV2, MatterSim, MACE, and CHGNet—on 2429 crystalline materials from the Open Quantum Materials Database. Models were used to compute atomic forces in displaced supercells, derive interatomic force constants (IFCs), and predict phonon properties including lattice thermal conductivity (LTC), compared with density functional theory and experimental data. The EquiformerV2 pretrained model trained on the OMat24 dataset exhibits strong performance in predicting atomic forces and third‐order IFCs, while its fine‐tuned counterpart consistently outperforms other models in predicting second‐order IFCs, LTC, and other phonon properties. Although MACE and CHGNet demonstrated comparable force prediction accuracy to EquiformerV2, notable discrepancies in IFC fitting led to poor LTC predictions. Conversely, MatterSim, despite lower force accuracy, achieved intermediate IFC predictions, suggesting error cancellation and complex relationships between force accuracy and phonon predictions. This benchmark guides the evaluation and selection of uMLPs for high‐throughput screening of materials with targeted thermal transport properties. 

Overview of the MatterTune framework, which enables flexible and accurate fine-tuning of pre-trained atomistic foundation models for materials science.