Ab initio molecular dynamics (AIMD) simulation is widely employed in studying diffusion mechanisms and in quantifying diffusional properties of materials. However, AIMD simulations are often limited to a few hundred atoms and a short, sub-nanosecond physical timescale, which leads to models that include only a limited number of diffusion events. As a result, the diffusional properties obtained from AIMD simulations are often plagued by poor statistics. In this paper, we reexamine the process to estimate diffusivity and ionic conductivity from the AIMD simulations and establish the procedure to minimize the fitting errors. In addition, we propose methods for quantifying the statistical variance of the diffusivity and ionic conductivity from the number of diffusion events observed during the AIMD simulation. Since an adequate number of diffusion events must be sampled, AIMD simulations should be sufficiently long and can only be performed on materials with reasonably fast diffusion. We chart the ranges of materials and physical conditions that can be accessible by AIMD simulations in studying diffusional properties. Our work provides the foundation for quantifying the statistical confidence levels of diffusion results from AIMD simulations and for correctly employing this powerful technique.

Footwear, carpet, automotive interiors, and multilayer packaging are examples of products manufactured from several types of polymers whose inextricability poses substantial challenges for recycling at the end of life. Here, we show that chemical circularity in mixed-polymer recycling becomes possible by controlling the rates of depolymerization of polydiketoenamines (PDK) over several orders of magnitude through molecular engineering. Stepwise deconstruction of mixed-PDK composites, laminates, and assemblies is chemospecific, allowing a prescribed subset of monomers, fillers, and additives to be recovered under pristine condition at each stage of the recycling process. We provide a theoretical framework to understand PDK depolymerization via acid-catalyzed hydrolysis and experimentally validate trends predicted for the rate-limiting step. The control achieved by PDK resins in managing chemical and material entropy points to wide-ranging opportunities for pairing circular design with sustainable manufacturing.

The design of circular polymers has emerged as a necessity due to the lack of efficient recycling methods for many commodity plastics, particularly those used in durable products. Among the promising circular polymers, polydiketoenamines (PDKs) stand out for their ability to undergo highly selective depolymerization in strong acid, allowing monomers to be recovered from additives and fillers. Varying the triketone monomer in PDK variants is known to strongly affect the depolymerization rate; however, it remains unclear how the chemistry of the cross-linker, far from the reaction center, affects the depolymerization rate. Notably, we found that a proximal amine in the cross-linker dramatically accelerates PDK depolymerization when compared to cross-linkers obviating this functionality. Moreover, the spacing between this amine and the diketoenamine bond offers a previously unexplored opportunity to tune PDK depolymerization rates. In this way, the molecular basis for PDK circularity is revealed and further suggests new targets for the amine monomer design to diversify PDK properties, while ensuring circularity in chemical recycling.

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Hydrolysis reactions are ubiquitous in biological, environmental, and industrial chemistry. Density functional theory (DFT) is commonly employed to study the kinetics and reaction mechanisms of hydrolysis processes. Here, we present a new dataset, Barrier Heights for HydrOlysis - 36 (BH2O-36), to enable the design of density functional approximations (DFA) and the rational selection of DFAs for applications in aqueous chemistry. BH2O-36 consists of 36 diverse organic and inorganic forward and reverse hydrolysis reactions with reference energy barriers calculated at the CCSD(T)/CBS level. Using BH2O-36, we evaluate 63 DFAs. In terms of mean absolute error (MAE) and mean relative absolute error (MRAE), wB97M-V is the best-performing DFA tested, while MN12-L-D3(BJ) is the best-performing pure (non-hybrid) DFA. Broadly, we find that range-separated hybrid DFAs are necessary to approach chemical accuracy (0.043 eV). Although the best-performing DFAs include a dispersion correction to account for long-range interactions, we find that dispersion corrections do not generally improve MAE or MRAE for this dataset.

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Hydrolysis reactions are ubiquitous in biological, environmental, and industrial chemistry. Density functional theory (DFT) is commonly employed to study the kinetics and reaction mechanisms of hydrolysis processes. Here, we present a new data set, Barrier Heights for HydrOlysis -36 (BH2O-36), to enable the design of density functional approximations (DFAs) and the rational selection of DFAs for applications in aqueous chemistry. BH2O-36 consists of 36 diverse organic and inorganic forward and reverse hydrolysis reactions with reference energy barriers ΔE ‡ calculated at the CCSD(T)/CBS level. Using BH2O-36, we evaluate 63 DFAs. In terms of mean absolute error (MAE) and mean relative absolute error (MRAE), ωB97M-V is the best-performing DFA tested, while MN12-L-D3(BJ) is the best-performing pure (nonhybrid) DFA. Broadly, we find that range-separated hybrid DFAs are necessary to approach chemical accuracy (0.043 eV). Although the best-performing DFAs include a dispersion correction to account for longrange interactions, we find that dispersion corrections do not generally improve MAE or MRAE for this data set.