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An equiatomic CrCoNi medium-entropy alloy was subjected to high-energy shot peening (HESP) to fabricate a gradient nanostructure (GNS) in this work. The microstructures of the GNS samples at different depths within the deformed layer were thoroughly investigated. The microstructure exhibited a transformation from unstressed coarse grains to deformed coarse grains, followed by the formation of ultrafine grains, and ultimately reaching a final nanocrystalline structure with a uniform size of approximately 50 nm. Detailed structural analysis indicated that the deformation process was primarily influenced by the interaction between dislocations and deformation twins, which was attributed to the low stacking fault energy (SFE) of the alloy. The nanocrystalline mechanism was divided into three stages. In the coarse-grained deformation stage, the dislocation band divided twin/matrix lamellae into sub-segments, and the cross twin divided coarse grains into ultrafine grains simultaneously. In the ultrafine grain deformation stage, dislocations were arranged around the deformation twins in order to break the twins to form incoherent boundaries, destroying the coherent relationship between the twin and matrix. Finally, in the nanocrystalline deformation stage, the nanocrystalline structure was further divided into smaller segments to accommodate additional strains through the interaction between dislocations and twins.
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http://dx.doi.org/10.3390/nano13131954 | DOI Listing |
J Chem Phys
September 2025
Department of Mathematics and Computer Science, Freie Universität, Berlin, Germany.
Coarse-grained (CG) molecular dynamics simulations extend the length and time scales of atomistic simulations by replacing groups of correlated atoms with CG beads. Machine-learned coarse-graining (MLCG) has recently emerged as a promising approach to construct highly accurate force fields for CG molecular dynamics. However, the calibration of MLCG force fields typically hinges on force matching, which demands extensive reference atomistic trajectories with corresponding force labels.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
September 2025
Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands.
Multivalent binding and the resulting dynamical clustering of receptors and ligands are known to be key features in biological interactions. For optimizing biomaterials capable of similar dynamical features, it is essential to understand the first step of these interactions, namely the multivalent molecular recognition between ligands and cell receptors. Here, we present the reciprocal cooperation between dynamic ligands in supramolecular polymers and dynamic receptors in model cell membranes, determining molecular recognition and multivalent binding via receptor clustering.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
September 2025
Generalized visual grounding tasks, including Generalized Referring Expression Comprehension (GREC) and Segmentation (GRES), extend the classical visual grounding paradigm by accommodating multi-target and non-target scenarios. Specifically, GREC focuses on accurately identifying all referential objects at the coarse bounding box level, while GRES aims for achieve fine-grained pixel-level perception. However, existing approaches typically treat these tasks independently, overlooking the benefits of jointly training GREC and GRES to ensure consistent multi-granularity predictions and streamline the overall process.
View Article and Find Full Text PDFJ Chem Phys
September 2025
Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics, and James Franck Institute, The University of Chicago, 5735 S. Ellis Ave., SCL 123, Chicago, Illinois 60637, USA.
Molecular dynamics simulations are essential for studying complex molecular systems, but their high computational cost limits scalability. Coarse-grained (CG) models reduce this cost by simplifying the system, yet traditional approaches often fail to maintain dynamic consistency, compromising their reliability in kinetics-driven processes. Here, we introduce an adversarial training framework that aligns CG trajectory ensembles with all-atom (AA) reference dynamics, ensuring both thermodynamic and kinetic fidelity.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
School of Information Science and Automation, Northeastern University, Shenyang, 110819 China.
Accurate prediction of drug-target interactions (DTIs) is crucial for improving the efficiency and success rate of drug development. Despite recent advancements, existing methods often fail to leverage interaction features at multiple granular levels, resulting in suboptimal data utilization and limited predictive performance. To address these challenges, we propose CF-DTI, a coarse-to-fine drug-target interaction model that integrates both coarse-grained and fine-grained features to enhance predictive accuracy.
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