Water-like Anomalies and Phase Behavior of a Pair Potential that Stabilizes Diamond

Targeted Discovery of Low-Coordinated Crystal Structures via Tunable Particle Interactions

Particles interacting via isotropic, multiwell pair potentials have been shown to self-assemble into a range of crystal structures, yet how the characteristics of the underlying interaction potential give rise to the resultant structure remains largely unknown. We have thus developed a functional form for the interaction potential in which all features can be tuned independently. We perform continuous parameter space searches by systematically changing pairs of parameters, controlling the various features of the interaction potential. By enforcing a repulsive first well (controlling particle interactions of the first neighbor shell), we stimulate the formation of low-coordinated assemblies. We report the self-assembly of 20 previously unknown crystal structure types, 14 of which have low coordination numbers. Despite limiting the search to a small region of the vast parameter space of possible particle interactions, a wealth of complexity and symmetry is apparent within these crystal structures, which include clathrates with empty cages and low-symmetry structures. Our findings suggest that an unknown number of previously undiscovered crystal structure configurations are possible through self-assembly, which can serve as interesting design targets for soft condensed matter synthesis.

Stability and Metastability of Liquid Water in a Machine-Learned Coarse-Grained Model with Short-Range Interactions

Coarse-grained water models are ∼100 times more efficient than all-atom models, enabling simulations of supercooled water and crystallization. The machine-learned monatomic model ML-BOP reproduces the experimental equation of state (EOS) and ice-liquid thermodynamics at 0.1 MPa on par with the all-atom TIP4P/2005 and TIP4P/Ice models. These all-atom models were parametrized using high-pressure experimental data and are either accurate for water's EOS (TIP4P/2005) or ice-liquid equilibrium (TIP4P/Ice). ML-BOP was parametrized from temperature-dependent ice and liquid experimental densities and melting data at 0.1 MPa; its only pressure training is from compression of TIP4P/2005 ice at 0 K. Here we investigate whether ML-BOP replicates the experimental EOS and ice-water thermodynamics along all pressures of ice I. We find that ML-BOP reproduces the temperature, enthalpy, entropy, and volume of melting of hexagonal ice up to 400 MPa and the EOS of water along the melting line with an accuracy that rivals that of both TIP4P/2005 and TIP4P/Ice. We interpret that the accuracy of ML-BOP originates from its ability to capture the shift between compact and open local structures to changes in pressure and temperature. ML-BOP reproduces the sharpening of the tetrahedral peak of the pair distribution function of water upon supercooling, and its pressure dependence. We characterize the region of metastability of liquid ML-BOP with respect to crystallization and cavitation. The accessibility of ice crystallization to simulations of ML-BOP, together with its accurate representation of the thermodynamics of water, makes it promising for investigating the interplay between anomalies, glass transition, and crystallization under conditions challenging to access through experiments.

Precise determination of pair interactions from pair statistics of many-body systems in and out of equilibrium

The determination of the pair potential v(r) that accurately yields an equilibrium state at positive temperature T with a prescribed pair correlation function g_{2}(r) or corresponding structure factor S(k) in d-dimensional Euclidean space R^{d} is an outstanding inverse statistical mechanics problem with far-reaching implications. Recently, Zhang and Torquato [Phys. Rev. E 101, 032124 (2020)2470-004510.1103/PhysRevE.101.032124] conjectured that any realizable g_{2}(r) or S(k) corresponding to a translationally invariant nonequilibrium system can be attained by a classical equilibrium ensemble involving only (up to) effective pair interactions. Testing this conjecture for nonequilibrium systems as well as for nontrivial equilibrium states requires improved inverse methodologies. We have devised an optimization algorithm to precisely determine effective pair potentials that correspond to pair statistics of general translationally invariant disordered many-body equilibrium or nonequilibrium systems at positive temperatures. This methodology utilizes a parameterized family of pointwise basis functions for the potential function whose initial form is informed by small-, intermediate- and large-distance behaviors dictated by statistical-mechanical theory. Subsequently, a nonlinear optimization technique is utilized to minimize an objective function that incorporates both the target pair correlation function g_{2}(r) and structure factor S(k) so that the small intermediate- and large-distance correlations are very accurately captured. To illustrate the versatility and power of our methodology, we accurately determine the effective pair interactions of the following four diverse target systems: (1) Lennard-Jones system in the vicinity of its critical point, (2) liquid under the Dzugutov potential, (3) nonequilibrium random sequential addition packing, and (4) a nonequilibrium hyperuniform "cloaked" uniformly randomized lattice. We found that the optimized pair potentials generate corresponding pair statistics that accurately match their corresponding targets with total L_{2}-norm errors that are an order of magnitude smaller than that of previous methods. The results of our investigation lend further support to the Zhang-Torquato conjecture. Furthermore, our algorithm will enable one to probe systems with identical pair statistics but different higher-body statistics, which will shed light on the well-known degeneracy problem of statistical mechanics.

Thermally induced amorphous to amorphous transition in hot-compressed silica glass

In situ Raman and Brillouin light scattering techniques were used to study thermally induced high-density amorphous (HDA) to low-density amorphous (LDA) transition in silica glass densified in hot compression (up to 8 GPa at 1100 °C). Hot-compressed silica samples are shown to retain structural and mechanical stability through 600 °C or greater, with reduced sensitivity in elastic response to temperature as compared with pristine silica glass. Given sufficient thermal energy to overcome the energy barrier, the compacted structure of the HDA silica reverts back to the LDA state. The onset temperature for the HDA to LDA transition depends on the degree of densification during hot compression, commencing at lower temperatures for samples with higher density, but all finishing within a temperature range of 250-300 °C. Our studies show that the HDA to LDA transition at high temperatures in hot-compressed samples is different from the gradual changes starting from room temperature in cold-compressed silica glass, indicating greater structural homogeneity achieved by hot compression. Furthermore, the structure and properties of hot-compressed silica glass change continuously during the thermally induced HDA to LDA transition, in contrast to the abrupt and first-order-like polyamorphic transitions in amorphous ice. Different HDA to LDA transition mechanisms in amorphous silica and amorphous ice are explained by their different energy landscapes.

Self-Assembly of Mesophases from Nanoparticles

A growing number of crystalline and quasi-crystalline structures have been formed by coating nanoparticles with ligands, polymers, and DNA. The design of nanoparticles that assemble into mesophases, such as those formed by block copolymers, would combine the order, mobility, and stimuli responsive properties of mesophases with the electronic, magnetic, and optical properties of nanoparticles. Here we use molecular simulations to demonstrate that binary mixtures of unbound particles with simple short-ranged pair interactions produce the same mesophases as block copolymers and surfactants, including lamellar, hexagonal, gyroid, body-centered cubic, face-centered cubic, perforated lamellar, and semicrystalline phases. The key to forming the mesophases is the frustrated attraction between particles of different types, achieved through control over interparticle size and over strength and softness of the interaction. Experimental design of nanoparticles with effective interactions described by the potentials of this work would provide a distinct, robust route to produce ordered tunable liquid crystalline mesophases from nanoparticles.