Solid materials with multiple observable phases can restructure in response to a change in temperature, fundamentally altering the materials' properties. This temperature-mediated solid transformation occurs primarily because of a difference in entropy between the two crystal forms. In this study, we examine for the first time the ability of classical point-charge molecular dynamics simulations to compute entropy and enthalpy differences between solid forms of a range of organic molecules and ultimately predict temperaturemediated restructuring events. Twelve polymorphic organic small molecule systems with known temperature-mediated transformations were modeled with the point-charge OPLS-AA potential. Relative entropies and free energies between different solid forms were estimated by computing the stability as a function of temperature from 0 K up to ambient conditions using molecular dynamics simulations. These simulations correctly found the experimental high temperature solid form to have an entropy larger than that of the low temperature form in all systems examined. The magnitude of the temperature/entropy contributions to the free energy at ambient conditions is generally larger than the change in enthalpy difference. We also find that free energy differences between polymorphs computed with a less expensive quasi-harmonic approximation are within 0.07 kcal•mol −1 at all temperatures up to 300 K in the small rigid molecules examined. However, the molecular dynamics free energies deviate from the quasi-harmonic approximation in the more flexible molecules and systems with disordered crystals by as much as 0.37 kcal•mol −1 . Finally, we demonstrate that at ambient conditions multiple lattice energy minima can convert into the same crystal ensemble due to easily kinetically accessible transitions between similar structures when thermal motions are present.
A novel algorithm is presented that achieves temporal acceleration during kinetic Monte Carlo (KMC) simulations of surface catalytic processes. This algorithm allows for the direct simulation of reaction networks containing kinetic processes occurring on vastly disparate time scales which computationally overburden standard KMC methods. Previously developed methods for temporal acceleration in KMC were designed for specific systems and often require a priori information from the user such as identifying the fast and slow processes. In the approach presented herein, quasi-equilibrated processes are identified automatically based on previous executions of the forward and reverse reactions. Temporal acceleration is achieved by automatically scaling the intrinsic rate constants of the quasi-equilibrated processes, bringing their rates closer to the time scales of the slow kinetically relevant nonequilibrated processes. All reactions are still simulated directly, although with modified rate constants. Abrupt changes in the underlying dynamics of the reaction network are identified during the simulation, and the reaction rate constants are rescaled accordingly. The algorithm was utilized here to model the Fischer-Tropsch synthesis reaction over ruthenium nanoparticles. This reaction network has multiple time-scale-disparate processes which would be intractable to simulate without the aid of temporal acceleration. The accelerated simulations are found to give reaction rates and selectivities indistinguishable from those calculated by an equivalent mean-field kinetic model. The computational savings of the algorithm can span many orders of magnitude in realistic systems, and the computational cost is not limited by the magnitude of the time scale disparity in the system processes. Furthermore, the algorithm has been designed in a generic fashion and can easily be applied to other surface catalytic processes of interest.
We examine the free energies of three benzene polymorphs as a function of temperature in the point-charge OPLS-AA and GROMOS54A7 potentials as well as the polarizable AMOEBA09 potential. For this system, using a polarizable Hamiltonian instead of the cheaper point-charge potentials is shown to have a significantly smaller effect on the stability at 250 K than on the lattice energy at 0 K. The benzene I polymorph is found to be the most stable crystal structure in all three potentials examined and at all temperatures examined. For each potential, we report the free energies over a range of temperatures and discuss the added value of using full free energy methods over the minimized lattice energy to determine the relative crystal stability at finite temperatures. The free energies in the polarizable Hamiltonian are efficiently calculated using samples collected in a cheaper point-charge potential. The polarizable free energies are estimated from the point-charge trajectories using Boltzmann reweighting with MBAR. The high configuration-space overlap necessary for efficient Boltzmann reweighting is achieved by designing point-charge potentials with intramolecular parameters matching those in the expensive polarizable Hamiltonian. Finally, we compare the computational cost of this indirect reweighted free energy estimate to the cost of simulating directly in the expensive polarizable Hamiltonian.
Crystal structure prediction (CSP) calculations can reduce risk and improve efficiency during drug development. Traditionally, CSP calculations use lattice energies computed through density functional theory. While this approach is often successful in predicting the low energy structures, it neglects the crucial role of thermal effects on polymorph stabilities. In the present study, we develop a robust and efficient protocol for predicting the relative stability of polymorphs at different temperatures. The protocol is executed on a highly parallel cloud computing infrastructure to produce results at time scales useful for drug development timelines. We demonstrate this protocol on molecule XXIII from the sixth crystal structure prediction blind test. Our results predict that Form D is the most stable experimentally observed polymorph at ambient temperature and Form C is the most stable at low temperature consistent with experiments also conducted in the present study.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.