Advances in theory and algorithms for electronic structure calculations must be incorporated into program packages to enable them to become routinely used by the broader chemical community. This work reviews advances made over the past five years or so that constitute the major improvements contained in a new release of the Q-Chem quantum chemistry package, together with illustrative timings and applications. Specific developments discussed include fast methods for density functional theory calculations, linear scaling evaluation of energies, NMR chemical shifts and electric properties, fast auxiliary basis function methods for correlated energies and gradients, equation-of-motion coupled cluster methods for ground and excited states, geminal wavefunctions, embedding methods and techniques for exploring potential energy surfaces.

We present a new approach for calculating reaction coordinates in complex systems. The new method is based on transition path sampling and likelihood maximization. It requires fewer trajectories than a single iteration of existing procedures, and it applies to both low and high friction dynamics. The new method screens a set of candidate collective variables for a good reaction coordinate that depends on a few relevant variables. The Bayesian information criterion determines whether additional variables significantly improve the reaction coordinate. Additionally, we present an advantageous transition path sampling algorithm and an algorithm to generate the most likely transition path in the space of collective variables. The method is demonstrated on two systems: a bistable model potential energy surface and nucleation in the Ising model. For the Ising model of nucleation, we quantify for the first time the role of nuclei surface area in the nucleation reaction coordinate. Surprisingly, increased surface area increases the stability of nuclei in two dimensions but decreases nuclei stability in three dimensions.

The current scale of plastics production and the accompanying waste disposal problems represent a largely untapped opportunity for chemical upcycling. Tandem catalytic conversion by platinum supported on γ-alumina converts various polyethylene grades in high yields (up to 80 weight percent) to low-molecular-weight liquid/wax products, in the absence of added solvent or molecular hydrogen, with little production of light gases. The major components are valuable long-chain alkylaromatics and alkylnaphthenes (average ~C30, dispersity Ð = 1.1). Coupling exothermic hydrogenolysis with endothermic aromatization renders the overall transformation thermodynamically accessible despite the moderate reaction temperature of 280°C. This approach demonstrates how waste polyolefins can be a viable feedstock for the generation of molecular hydrocarbon products.

Overconsumption of single-use plastics is creating a global waste catastrophe, with widespread environmental, economic, and health-related consequences. Inspired by the benefits of processive enzyme-catalyzed conversions of biomacromolecules and guided by spectroscopic interrogations of conformation and dynamics of polymer-surface interactions, we have developed the selective hydrogenolysis of high density polyethylene into a narrow distribution of diesel and lubricant-range alkanes catalyzed by an ordered, mesoporous shell/active site/core catalyst architecture. Solid-state nuclear magnetic resonance investigations of polymer chains adsorbed onto solid materials reveal that an appropriately ordered, porous support orients polymer chains into an all-anti conformation, while measurements of polymer dynamics reveal that long hydrocarbon macromolecules readily move within the pores, with a subsequent escape being inhibited by polymer-surface interactions. These interactions and dynamic behavior resemble the binding and translocation of macromolecules in the catalytic cleft of processive enzymes. Thus, transfer of these features to a mesoporous silica material incorporating catalytic platinum sites for carbon-carbon bond hydrogenolysis of polyethylene provides a reliable stream of alkane products through a processive process.

Interpolation methods such as the nudged elastic band and string methods are widely used for calculating minimum energy pathways and transition states for chemical reactions. Both methods require an initial guess for the reaction pathway. A poorly chosen initial guess can cause slow convergence, convergence to an incorrect pathway, or even failed electronic structure force calculations along the guessed pathway. This paper presents a growing string method that can find minimum energy pathways and transition states without the requirement of an initial guess for the pathway. The growing string begins as two string fragments, one associated with the reactants and the other with the products. Each string fragment is grown separately until the fragments converge. Once the two fragments join, the full string moves toward the minimum energy pathway according to the algorithm for the string method. This paper compares the growing string method to the string method and to the nudged elastic band method using the alanine dipeptide rearrangement as an example. In this example, for which the linearly interpolated guess is far from the minimum energy pathway, the growing string method finds the saddle point with significantly fewer electronic structure force calculations than the string method or the nudged elastic band method.

Methane hydrates are ice-like inclusion compounds with importance to the oil and natural gas industry, global climate change, and gas transportation and storage. The molecular mechanism by which these compounds form under conditions relevant to industry and nature remains mysterious. To understand the mechanism of methane hydrate nucleation from supersaturated aqueous solutions, we performed simulations at controlled and realistic supersaturation. We found that critical nuclei are extremely large and that homogeneous nucleation rates are extremely low. Our findings suggest that nucleation of methane hydrates under these realistic conditions cannot occur by a homogeneous mechanism.

The freezing of water affects the processes that determine Earth's climate. Therefore, accurate weather and climate forecasts hinge on good predictions of ice nucleation rates. Such rate predictions are based on extrapolations using classical nucleation theory, which assumes that the structure of nanometre-sized ice crystallites corresponds to that of hexagonal ice, the thermodynamically stable form of bulk ice. However, simulations with various water models find that ice nucleated and grown under atmospheric temperatures is at all sizes stacking-disordered, consisting of random sequences of cubic and hexagonal ice layers. This implies that stacking-disordered ice crystallites either are more stable than hexagonal ice crystallites or form because of non-equilibrium dynamical effects. Both scenarios challenge central tenets of classical nucleation theory. Here we use rare-event sampling and free energy calculations with the mW water model to show that the entropy of mixing cubic and hexagonal layers makes stacking-disordered ice the stable phase for crystallites up to a size of at least 100,000 molecules. We find that stacking-disordered critical crystallites at 230 kelvin are about 14 kilojoules per mole of crystallite more stable than hexagonal crystallites, making their ice nucleation rates more than three orders of magnitude higher than predicted by classical nucleation theory. This effect on nucleation rates is temperature dependent, being the most pronounced at the warmest conditions, and should affect the modelling of cloud formation and ice particle numbers, which are very sensitive to the temperature dependence of ice nucleation rates. We conclude that classical nucleation theory needs to be corrected to include the dependence of the crystallization driving force on the size of the ice crystallite when interpreting and extrapolating ice nucleation rates from experimental laboratory conditions to the temperatures that occur in clouds.

Cellulose is a crystalline polymer of β1,4-D-glucose that is difficult to deconstruct to sugars by enzymes. The recalcitrance of cellulose microfibrils is a function of both the shape of cellulose microfibrils and the intrinsic work required to decrystallize individual chains, the latter of which is calculated here from the surfaces of four crystalline cellulose polymorphs: cellulose Iβ, cellulose Iα, cellulose II, and cellulose III(I). For edge chains, the order of decrystallization work is as follows (from highest to lowest): Iβ, Iα, ΙΙΙ(Ι), and II. For cellulose Iβ, we compare chains from three different locations on the surface and find that an increasing number of intralayer hydrogen bonds (from 0 to 2) increases the intrinsic decrystallization work. From these results, we propose a microkinetic model for the deconstruction of cellulose (and chitin) by processive enzymes, which when taken with a previous study [Horn et al. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 18089] identifies the thermodynamic and kinetic attributes of enzyme and substrate engineering for enhanced cellulose (or chitin) conversion. Overall, this study provides new insights into the molecular interactions that form the structural basis of cellulose, which is the primary building block of plant cell walls, and highlights the need for experimentally determining microfibril shape at the nanometer length scale when comparing conversion rates of cellulose polymorphs by enzymes.