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Coarse-grained MD simulations reveal that the crumpling behavior of graphene nanoribbons depends strongly on width and aspect ratio, leading to distinct deformation modes, structural ordering, and mechanical responses. 

This first-principles study investigates the interactions between amino acids and various types of montmorillonite clay surfaces, including a pristine surface, a surface with an oxygen vacancy, a surface with a silicon vacancy, and an Fe-doped surface. Our results show that all clay surfaces exhibit negative binding energies, indicating that the interaction between clay and amino acids is thermodynamically favorable. Among them, the surface with a Si vacancy displays the most negative binding energy, corresponding to the strongest interaction. We also examine the reactions between two alanine molecules to form a dipeptide molecule through the elimination of a water molecule in the absence of clay surfaces. The transition state search suggests that a proton transfer plays a critical role in the peptide bond formation based on structural and energetic features observed along the reaction path. Circular dichroism spectra computed for reactant, intermediate, and product states show distinct chiral signatures. Wave packet dynamics calculations indicate that quantum tunneling might be the mechanism underlying the reduced activation energy at low temperatures. These findings offer insight into the physicochemical processes at clay–amino acid interfaces and support the design of clay-based materials with applications in biotechnology and prebiotic chemistry. 

Supramolecular polymer blends (SPBs) represent a versatile class of polymers whose morphology directly determines their macroscopic properties. However, rational design of SPBs remains hindered by the lack of predictive models describing how molecular features and intermolecular interactions determine morphology. Here, we report a data-driven high-throughput workflow integrating modular synthesis, robotic sample formulation and processing, automated morphology characterization, and machine learning (ML) for SPBs discovery. Using a plug-and-play modular synthetic strategy, 33 hydrogen-bonding end-functional homopolymer precursors were prepared and orthogonally paired to fabricate 260 SPBs within one day. A custom automated atomic force microscopy (AFM) protocol enabled systematic morphological characterization, producing 2340 images with little human intervention. Average phase separation sizes (e.g. domain spacings) was extracted from processed AFM data using multiple complementary approaches and applied to ML model training. Leveraging the high-throughput sample formation and characterization, a high-quality database was curated for SPBs, allowing training of ML models. Guided by support vector regression (SVR) model, target morphologies of 50, 100, and 150 nm were successfully predicted and experimentally validated. This work demonstrates the potential of coupling high-throughput experimentation with ML to accelerate polymer blends phase discovery, providing one of the first large-scale, experimentally derived datasets specifically designed for supramolecular polymer research. 

A modeling-driven materials-by-design framework is provided to explore the multifunctional performance of conjugated polymers (CPs), offering new insights for the design and development of advanced CP-based materials and devices. 

Semiconducting conjugated polymers (CPs) are pivotal in advancing organic electronics, offering tunable properties for solar cells and field-effect transistors. Here, we carry out first-principle calculations to study individual cis-polyacetylene (cis-PA) oligomers and their ensembles. The ground electronic structures are obtained using density functional theory (DFT), and excited state dynamics are explored by computing nonadiabatic couplings (NACs) between electronic and nuclear degrees of freedom. We compute the nonradiative relaxation of charge carriers and photoluminescence (PL) using the Redfield theory. Our findings show that electrons relax faster than holes. The ensemble of oligomers shows faster relaxation compared to the single oligomer. The calculated PL spectra show features from both interband and intraband transitions. The ensemble shows broader line widths, redshift of transition energies, and lower intensities compared to the single oligomer. This comparative study suggests that the dispersion forces and orbital hybridizations between chains are the leading contributors to the variation in PL. It provides insights into the fundamental behaviors of CPs and the molecular-level understanding for the design of more efficient optoelectronic devices.