A thermodynamically guided approach enables the solvent-based recycling of multilayer plastic packaging films.

The rates of Brønsted-acid-catalyzed reactions of ethyl tert-butyl ether, tert-butanol, levoglucosan, 1,2-propanediol, fructose, cellobiose, and xylitol were measured in solvent mixtures of water with three polar aprotic cosolvents: γ-valerolactone; 1,4-dioxane; and tetrahydrofuran. As the water content of the solvent environment decreases, reactants with more hydroxyl groups have higher catalytic turnover rates for both hydrolysis and dehydration reactions. We present classical molecular dynamics simulations to explain these solvent effects in terms of three simulation-derived observables: (1) the extent of water enrichment in the local solvent domain of the reactant; (2) the average hydrogen bonding lifetime between water molecules and the reactant; and (3) the fraction of the reactant accessible surface area occupied by hydroxyl groups, all as a function of solvent composition. We develop a model, constituted by linear combinations of these three observables, that predicts experimentally determined rate constants as a function of solvent composition for the entire set of acid-catalyzed reactions.

Solvent-mediated, acid-catalyzed reaction rates relevant to the upgrading of biomass into high-value chemicals are accurately predicted using a combination of molecular dynamics simulations and 3D convolutional neural networks.

Gold nanoparticles (GNPs) are versatile materials suitable for various biological applications due to their tunable surface properties, but structure-function relationships between specific GNP components and GNP behavior are largely lacking. In this work, atomistic molecular dynamics simulations were used to study the influence of gold core morphology, size, and ligand length on the structure of uniformly nonpolar alkanethiol monolayer-protected GNPs in water. Using a generalized system preparation workflow, three gold core models were selected for this study: (1) a uniformly spherical hollow gold core, (2) a spherical gold core cut from a bulk gold lattice, and (3) a faceted gold core obtained from variance-constrained semigrand-canonical simulations. Independent of the gold core morphology, we found that long alkanethiol ligands exhibit increased ligand order and form quasi-crystalline domains, or bundles, in which ligands orient in the same direction, leading to asymmetric monolayer structures. Faceted gold cores promote the formation of ligand bundles for short ligand lengths, but the influence of the gold core is diminished for long ligand lengths. We used a clustering algorithm to identify the subpopulation of bundled ligands and found that bundling leads to heterogeneous surface properties, whereby bundled ligands have a higher ligand order and lower surface area accessible for solvent interactions. These findings demonstrate the importance of GNP compositional features on monolayer structure, which could impact GNPs interactions with other molecules.

Mixtures of chemical ingredients, such as formulations, are ubiquitous in materials science, but optimizing their properties remains challenging due to the vast design space. Computational approaches offer a promising solution to traverse this space while minimizing trial-and-error experimentation. Using high-throughput classical molecular dynamics simulations, we generated a comprehensive dataset of over 30,000 solvent mixtures to evaluate three machine learning approaches that connect molecular structure and composition to property: formulation descriptor aggregation (FDA), formulation graph (FG), and Set2Set-based method (FDS2S). Our results demonstrate that our new FDS2S approach outperforms other approaches in predicting simulation-derived properties. Formulation-property relationships can reveal important substructures and identify promising formulations at least two to three times faster than random guessing. The models show robust transferability to experimental datasets, accurately predicting properties across energy, pharmaceutical, and petroleum applications. Our research demonstrates the utility of high-throughput simulations and machine learning tools to design formulations with promising properties.

The development of next-generation polymers necessitates optimizing several key properties simultaneously, a task that is expensive and infeasible using traditional trial-and-error experimental approaches. A promising alternative is employing a combination of machine learning and physics-based tools to rapidly screen the polymer design space and provide suggestions of new polymers that meet the critical properties required for industrial applications. In this study, we introduce a comprehensive workflow that utilizes machine learning and molecular modeling approaches to design new polymers with the focus on improving five polymer properties: (1) glass transition temperature, (2) dielectric constant, (3) refractive index, (4) stress optic coefficient, and (5) linear coefficient of thermal expansion. Using a small dataset ( < 200 unique polymers), we developed quantitative structure-property relationships (QSPRs) models to accurately predict the experimental polymer properties for both homo- and co-polymer systems. We tested several ML algorithms and identified the best models for predicting these polymer properties, achieving test set R ² greater than 0.77 across all properties. We then explored new polymers by creating a library of over ∼10 000 homopolymers using R-group enumeration tools and applied the trained QSPR models to rapidly predict the five polymer properties. The predictions of QSPR models were used to create a multi-parameter optimization score, which helped downselect the large polymer space to ∼10 promising candidates. The properties of these selected polymer candidates were subsequently validated with classical molecular dynamics simulations and density functional theory, revealing a strong correlation with the QSPR model predictions. Finally, one of the top candidates was validated by experiments, which showed good agreement against QSPR and physics-based models. Our workflow underscores the power of combining data-driven and theoretical methods in the polymer design process given a small dataset size, offering a valuable resource for experimentalists looking to leverage computer-aided strategies in materials innovation.