Orbital-free density functional theory (OFDFT) is both grounded in quantum physics and suitable for direct simulation of thousands of atoms. This article describes the application of OFDFT for materials research over roughly the past two decades, highlighting computational studies that would have been impractical (or impossible) to perform with other techniques. In particular, we review the growing body of simulations of solids and liquids that have been conducted with planewave-pseudopotential (or related) techniques. We also provide an updated account of the fundamentals of OFDFT, emphasizing aspects—such as nonlocal density functionals for computing the kinetic energy of noninteracting electrons—that enabled much of the application work. The article concludes with a discussion of the OFDFT frontier, which contains brief descriptions of other topics at the forefront of OFDFT research.

We review two recent advances in coupled quantum mechanics/molecular mechanics (QM/MM) modeling for metallic materials. The QM/MM methods are formulated based on quantum mechanical charge density embedding. In the first method, QM/MM coupling is accomplished by an embedding potential evaluated via orbital-free density functional theory. The charge density embedding in the second QM/MM method is achieved through constrained density functional theory. The extension of QM/MM coupling to the quasicontinuum method is illustrated, offering a route toward quantum mechanical simulations of materials at micron scales and beyond. The theoretical formulations of the QM/MM methods are discussed in detail. We also provide some examples where the QM/MM methods have been applied to understand fundamental physics in a wide range of material problems, ranging from void formation, pipe diffusion along dislocation core, nanoindentation of thin films, hydrogen-assisted cracking, magnetism-induced plasticity to stress-controlled catalysis in metals. An outlook to future development of QM/MM methods for metals is envisioned.

Molecular dynamics (MD) is one of the most widely used techniques in computational materials science. By providing fully resolved trajectories, it allows for a natural description of static, thermodynamic, and kinetic properties. A major hurdle that has hampered the use of MD is the fact that the timescales that can be directly simulated are very limited, even when using massively parallel computers. In this study, we compare two time-parallelization approaches, parallel replica dynamics (ParRep) and parallel trajectory splicing (ParSplice), that were specifically designed to address this issue for rare event systems by leveraging parallel computing resources. Using simulations of the relaxation of small disordered platinum nanoparticles, a comparative performance analysis of the two methods is presented. The results show that ParSplice can significantly outperform ParRep in the common case where the trajectory remains trapped for a long time within a region of configuration space but makes rapid structural transitions within this region.

Accelerated Molecular Dynamics (AMD) is a class of MD-based algorithms for the long-time scale simulation of atomistic systems that are characterized by rare-event transitions. Temperature-Accelerated Dynamics (TAD), a traditional AMD approach, hastens state-to-state transitions by performing MD at an elevated temperature. Recently, Speculatively-Parallel TAD (SpecTAD) was introduced, allowing the TAD procedure to exploit parallel computing systems by concurrently executing in a dynamically generated list of speculative future states. Although speculation can be very powerful, it is not always the most efficient use of parallel resources. Here, we compare the performance of speculative parallelism with a replica-based technique, similar to the Parallel Replica Dynamics method. A hybrid SpecTAD approach is also presented, in which each speculation process is further accelerated by a local set of replicas. Overall, this work motivates the use of hybrid parallelism whenever possible, as some combination of speculation and replication is typically most efficient.

The temperature programmed molecular dynamics (TPMD) method is a recent addition to the list of rare-event simulation techniques for materials. Study of thermally-activated events that are rare at molecular dynamics (MD) timescales is possible with TPMD by employing a temperature program that raises the temperature in stages to a point where the transitions happen frequently. Analysis of the observed waiting time distribution yields a wealth of information including kinetic mechanisms in the material, their rate constants and Arrhenius parameters. The first part of this review covers the foundations of the TPMD method. Recent applications of TPMD are discussed to highlight its main advantages. These advantages offer the possibility for rapid construction of kinetic Monte Carlo (KMC) models of a chosen accuracy using TPMD. In this regards, the second part focuses on the latest developments on uncertainty measures for KMC models. The third part focuses on current challenges for the TPMD method and ways of resolving them.

The growth of Ag on ZnO was modeled using a reactive force field potential and a combination of molecular dynamics and adaptive kinetic Monte Carlo (AKMC) simulations. An adaptive lattice-based AKMC model is described as a method of extending timescales and length scales that can be simulated. Reusing previously found transitions to reduce computational time is discussed for both the lattice and off-lattice AKMC approaches. With these methods, growth of over 1 monolayer’s worth of Ag is simulated corresponding to a real deposition time of up to 0.1 s. The results show that the deposited silver aggregates on the surface through mainly single atom moves with few concerted motions. Initially silver adatoms do not agglomerate and the energy barriers for silver dimers to form are larger than for them to break apart. The first layer of silver grows as a series of connected regions rather than forming well-defined centro-symmetric islands.

We present a novel distributed-memory parallel implementation of the concurrent atomistic-continuum (CAC) method. Written mostly in Fortran 2008 and wrapped with a Python scripting interface, the CAC simulator in PyCAC runs in parallel using Message Passing Interface with a spatial decomposition algorithm. Built upon the underlying Fortran code, the Python interface provides a robust and versatile way for users to build system configurations, run CAC simulations, and analyze results. In this paper, following a brief introduction to the theoretical background of the CAC method, we discuss the serial algorithms of dynamic, quasistatic, and hybrid CAC, along with some programming techniques used in the code. We then illustrate the parallel algorithm, quantify the parallel scalability, and discuss some software specifications of PyCAC; more information can be found in the PyCAC user’s manual that is hosted on www.pycac.org.