Ice Phase Classification Made Easy with Score-Based Denoising.

Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and thermal fluctuations. For this purpose, both traditional order parameters and data-driven machine learning approaches have been employed, but they often rely on expert intuition, specific geometric information, or large training data sets.

Score Dynamics: Scaling Molecular Dynamics with Picoseconds Time Steps via Conditional Diffusion Model.

We propose score dynamics (SD), a general framework for learning accelerated evolution operators with large timesteps from molecular dynamics (MD) simulations. SD is centered around scores or derivatives of the transition log-probability with respect to the dynamical degrees of freedom. The latter play the same role as force fields in MD but are used in denoising diffusion probability models to generate discrete transitions of the dynamical variables in an SD time step, which can be orders of magnitude larger than a typical MD time step.

Spectral-partitioned Kohn-Sham density functional theory.

We introduce a general, variational scheme for systematic approximation of a given Kohn-Sham free-energy functional by partitioning the density matrix into distinct spectral domains, each of which may be spanned by an independent diagonal representation without requirement of mutual orthogonality. It is shown that by generalizing the entropic contribution to the free energy to allow for independent representations in each spectral domain, the free energy becomes an upper bound to the exact (unpartitioned) Kohn-Sham free energy, attaining this limit as the representations approach Kohn-Sham eigenfunctions. A numerical procedure is devised for calculation of the generalized entropy associated with spectral partitioning of the density matrix.

Properties of carbon up to 10 million kelvin from Kohn-Sham density functional theory molecular dynamics.

Accurately modeling dense plasmas over wide-ranging conditions of pressure and temperature is a grand challenge critically important to our understanding of stellar and planetary physics as well as inertial confinement fusion. In this work, we employ Kohn-Sham density functional theory (DFT) molecular dynamics (MD) to compute the properties of carbon at warm and hot dense matter conditions in the vicinity of the principal Hugoniot. In particular, we calculate the equation of state (EOS), Hugoniot, pair distribution functions, and diffusion coefficients for carbon at densities spanning 8 g/cm^{3} to 16 g/cm^{3} and temperatures ranging from 100 kK to 10 MK using the Spectral Quadrature method.

Accurate parameterization of the kinetic energy functional.

The absence of a reliable formulation of the kinetic energy density functional has hindered the development of orbital free density functional theory. Using the data-aided learning paradigm, we propose a simple prescription to accurately model the kinetic energy density of any system. Our method relies on a dictionary of functional forms for local and nonlocal contributions, which have been proposed in the literature, and the appropriate coefficients are calculated via a linear regression framework.

Accurate parameterization of the kinetic energy functional for calculations using exact-exchange.

Electronic structure calculations based on Kohn-Sham density functional theory (KSDFT) that incorporate exact-exchange or hybrid functionals are associated with a large computational expense, a consequence of the inherent cubic scaling bottleneck and large associated prefactor, which limits the length and time scales that can be accessed. Although orbital-free density functional theory (OFDFT) calculations scale linearly with system size and are associated with a significantly smaller prefactor, they are limited by the absence of accurate density-dependent kinetic energy functionals. Therefore, the development of accurate density-dependent kinetic energy functionals is important for OFDFT calculations of large realistic systems.

Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields.

Density functional tight binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard density functional theory (DFT) approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions.

Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks.

Microstructural evolution is a key aspect of understanding and exploiting the processing-structure-property relationship of materials. Modeling microstructure evolution usually relies on coarse-grained simulations with evolution principles described by partial differential equations (PDEs). Here we demonstrate that convolutional recurrent neural networks can learn the underlying physical rules and replace PDE-based simulations in the prediction of microstructure phenomena.

Metastable-solid phase diagrams derived from polymorphic solidification kinetics.

Nonequilibrium processes during solidification can lead to kinetic stabilization of metastable crystal phases. A general framework for predicting the solidification conditions that lead to metastable-phase growth is developed and applied to a model face-centered cubic (fcc) metal that undergoes phase transitions to the body-centered cubic (bcc) as well as the hexagonal close-packed phases at high temperatures and pressures. Large-scale molecular dynamics simulations of ultrarapid freezing show that bcc nucleates and grows well outside of the region of its thermodynamic stability.

Nanosecond Freezing of Water at High Pressures: Nucleation and Growth near the Metastability Limit.

The fundamental study of phase transition kinetics has motivated experimental methods toward achieving the largest degree of undercooling possible, more recently culminating in the technique of rapid, quasi-isentropic compression. This approach has been demonstrated to freeze water into the high-pressure ice VII phase on nanosecond timescales, with some experiments undergoing heterogeneous nucleation while others, in apparent contradiction, suggest a homogeneous nucleation mode. In this study, we show through a combination of theory, simulation, and analysis of experiments that these seemingly contradictory results are in agreement when viewed from the perspective of classical nucleation theory.

Space- and time-resolved small angle X-ray scattering to probe assembly of silver nanocrystal superlattices.

The structure of nanocrystal superlattices has been extensively studied and well documented, however, their assembly process is poorly understood. In this work, we demonstrate an in situ space- and time-resolved small angle X-ray scattering measurement that we use to probe the assembly of silver nanocrystal superlattices driven by electric fields. The electric field creates a nanocrystal flux to the surface, providing a systematic means to vary the nanocrystal concentration near the electrode and thereby to initiate nucleation and growth of superlattices in several minutes.

Magnetostructural Transition Kinetics in Shocked Iron.

A generalized Heisenberg model is implemented to study the effect of thermal magnetic disorder on kinetics of the Fe α-ε transition. The barrier to bulk martensitic displacement remains large in α-Fe shocked well past the phase line but is much reduced in the [001] α-ε boundary. The first result is consistent with observed overdriving to metastable α, while the second suggests structural instability, as implied by observation of a [001] shock transformation front without plastic relaxation.

Low-temperature criticality of martensitic transformations of Cu nanoprecipitates in α-Fe.

Nanoprecipitates form during nucleation of multiphase equilibria in phase segregating multicomponent systems. In spite of their ubiquity, their size-dependent physical chemistry, in particular, at the boundary between phases with incompatible topologies, is still rather arcane. Here, we use extensive atomistic simulations to map out the size-temperature phase diagram of Cu nanoprecipitates in α-Fe.

First-passage kinetic Monte Carlo method.

We present an efficient method for Monte Carlo simulations of diffusion-reaction processes. Introduced by us in a previous paper [Phys. Rev.

Thermodynamic ground states of platinum metal nitrides.

The thermodynamic stabilities of various phases of the nitrides of the platinum-metal elements are systematically studied using density functional theory. It is shown that for the nitrides of Rh, Pd, Ir, and Pt two new crystal structures, in which the metal ions occupy simple tetragonal lattice sites, have lower formation enthalpies at ambient conditions than any previously proposed structures. The region of stability with respect to those structures extends to 17 GPa for PtN2.

First-passage Monte Carlo algorithm: diffusion without all the hops.

We present a novel Monte Carlo algorithm for N diffusing finite particles that react on collisions. Using the theory of first-passage processes and time dependent Green's functions, we break the difficult N-body problem into independent single- and two-body propagations circumventing numerous diffusion hops used in standard Monte Carlo simulations. The new algorithm is exact, extremely efficient, and applicable to many important physical situations in arbitrary integer dimensions.

Synthesis and characterization of the nitrides of platinum and iridium.

Transition metal nitrides are of great technological and fundamental importance because of their strength and durability and because of their useful optical, electronic, and magnetic properties. We have evaluated a recently synthesized platinum nitride (PtN) that was shown to have a large bulk modulus, and we propose a structure that is isostructural with pyrite and has the stoichiometry PtN2. We have also synthesized a recoverable nitride of iridium under nearly the same conditions of pressure and temperature as PtN2.

Adaptive importance sampling Monte Carlo simulation of rare transition events.

We develop a general theoretical framework for the recently proposed importance sampling method for enhancing the efficiency of rare-event simulations [W. Cai, M. H.

Density-functional calculations of alpha, beta, gamma, delta, delta', and epsilon plutonium.

Total energies for the six known polymorphs of plutonium metal have been calculated within spin and orbital polarized density-functional theory as a function of lattice constant. Theoretical equilibrium volumes and bulk moduli correspond well with experimental data and the calculated total energies are consistent with the known phase diagram of Pu. It is shown that a preference for the formation of magnetic moments, increasing through the alpha-->beta-->gamma phases, explains their position in the ambient pressure phase diagram and their anomalous variation of atomic density.

Transformations in the medium-range order of fused silica under high pressure.

Molecular dynamics simulations of fused silica at shock pressures reproduce the experimental equation of state of this material and explain its characteristic shape. We demonstrate that shock waves modify the medium-range order of this amorphous system, producing changes that are only clearly revealed by its ring size distribution. The ring size distribution remains practically unchanged during elastic compression but varies continuously after the transition to the plastic regime.