How Accurate Can Crystal Structure Predictions Be for High-Energy Molecular Crystals?

Molecular crystals have shallow potential energy landscapes, with multiple local minima separated by very small differences in total energy. Predicting molecular packing and molecular conformation in the crystal generally requires ab initio methods of high accuracy, especially when polymorphs are involved. We used dispersion-corrected density functional theory (DFT-D) to assess the capabilities of an evolutionary algorithm (EA) for the crystal structure prediction (CSP) of well-known but challenging high-energy molecular crystals (HMX, RDX, CL-20, and FOX-7). While providing the EA with the experimental conformation of the molecule quickly re-discovers the experimental packing, it is more realistic to start instead from a naïve, flat, or neutral initial conformation, which reflects the limited experimental knowledge we generally have in the computational design of molecular crystals. By doing so, and using fully flexible molecules in fully variable unit cells, we show that the experimental structures can be predicted in fewer than 20 generations. Nonetheless, one must be aware that some molecular crystals have naturally hindered evolutions, requiring as many attempts as there are space groups of interest to predict their structures, and some may require the accuracy of all-electron calculations to discriminate between closely ranked structures. To save resources in this computationally demanding process, we showed that a hybrid xTB/DFT-D approach could be considered in a subsequent study to push the limits of CSP beyond 200+ atoms and for cocrystals.

Temperature-Transferable Coarse-Grained Model for Poly(propylene oxide) to Study Thermo-Responsive Behavior of Triblock Copolymers.

Thermo-responsive behavior of ethylene oxide (EO)-propylene oxide (PO) copolymers makes them suitable for many potential applications. Reproducing the origins of the tunable properties of EO-PO copolymers using coarse-grained (CG) models such as the MARTINI force field is critically important for building a better understanding of their behavior. In the present work, we have investigated the effects of coarse-graining on the water-polymer interaction across a temperature range. We compared the performance of different all-atom force fields to find the most appropriate one for the purpose of PO block parameterization in the MARTINI platform. We parameterized a CG temperature-dependent PO model based on the reproduction of the atomistic free energy of transfer of propylene oxide trimer from octane to water over a range of temperatures (20-60 °C) and compared the atomistic bond and angle distributions. Then, we used the model to study the effects of EO/PO ratio, molecular weight, and concentration on the thermo-responsive behavior of EO-PO copolymers in water. The results show an excellent agreement with experiments in different areas. Our temperature-dependent model reproduces (1) micellar phase above critical micelle temperature (CMT) and unimer phase below CMT for different Pluronics (a class of EO-PO triblock copolymers) spanning many EO/PO ratios and molecular weights; (2) spherical-to-rodlike micellar shape transition for Pluronics with 60 wt % of PO content or more; (3) diffusion coefficients for Pluronics with high PO content (P104 Pluronic with a PO mass of 3500 g mol-1) across a broad range of temperatures; and (4) micelle core size and micelle diameter similar to experimental results. Overall, our model improves the temperature sensitivity of EO-PO copolymers of existing models significantly, particularly for copolymers that are dominated by PO agents.

A simple transferable adaptive potential to study phase separation in large-scale xMgO-(1-x)SiO2 binary glasses.

A simple transferable adaptive model is developed and it allows for the first time to simulate by molecular dynamics the separation of large phases in the MgO-SiO2 binary system, as experimentally observed and as predicted by the phase diagram, meaning that separated phases have various compositions. This is a real improvement over fixed-charge models, which are often limited to an interpretation involving the formation of pure clusters, or involving the modified random network model. Our adaptive model, efficient to reproduce known crystalline and glassy structures, allows us to track the formation of large amorphous Mg-rich Si-poor nanoparticles in an Mg-poor Si-rich matrix from a 0.1MgO-0.9SiO2 melt.