ACEpotentials.jl: A Julia implementation of the atomic cluster expansion.

We introduce ACEpotentials.jl, a Julia-language software package that constructs interatomic potentials from quantum mechanical reference data using the Atomic Cluster Expansion [R. Drautz, Phys. Rev. B 99, 014104 (2019)]. As the latter provides a complete description of atomic environments, including invariance to overall translation and rotation as well as permutation of like atoms, the resulting potentials are systematically improvable and data efficient. Furthermore, the descriptor's expressiveness enables use of a linear model, facilitating rapid evaluation and straightforward application of Bayesian techniques for active learning. We summarize the capabilities of ACEpotentials.jl and demonstrate its strengths (simplicity, interpretability, robustness, performance) on a selection of prototypical atomistic modelling workflows.

Developments and further applications of ephemeral data derived potentials.

Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction.

Automatic differentiation for orbital-free density functional theory.

Differentiable programming has facilitated numerous methodological advances in scientific computing. Physics engines supporting automatic differentiation have simpler code, accelerating the development process and reducing the maintenance burden. Furthermore, fully differentiable simulation tools enable direct evaluation of challenging derivatives-including those directly related to properties measurable by experiment-that are conventionally computed with finite difference methods. Here, we investigate automatic differentiation in the context of orbital-free density functional theory (OFDFT) simulations of materials, introducing PROFESS-AD. Its automatic evaluation of properties derived from first derivatives, including functional potentials, forces, and stresses, facilitates the development and testing of new density functionals, while its direct evaluation of properties requiring higher-order derivatives, such as bulk moduli, elastic constants, and force constants, offers more concise implementations than conventional finite difference methods. For these reasons, PROFESS-AD serves as an excellent prototyping tool and provides new opportunities for OFDFT.

Random Structure Searching with Orbital-Free Density Functional Theory.

The properties of a material depend on how its atoms are arranged, and predicting these arrangements from first principles is a longstanding challenge. Orbital-free density functional theory provides a quantum-mechanical model based solely on the electron density, not individual wave functions. The resulting speedups make it attractive for random structure searching, whereby random configurations of atoms are relaxed to local minima in the energy landscape. We use this strategy to map the low-energy crystal structures of Li, Na, Mg, and Al at zero pressure. For Li and Na, our searching finds numerous close-packed polytypes of almost-equal energy, consistent with previous efforts to understand their low-temperature forms. For Mg and Al, the searching identifies the expected ground state structures unambiguously, in addition to revealing other low-energy structures. This new role for orbital-free density functional theory-particularly as continued advances make it accurate for more of the periodic table-will expedite crystal structure prediction over wide ranges of compositions and pressures.

libKEDF: An accelerated library of kinetic energy density functionals.

Kinetic energy density functionals (KEDFs) approximate the kinetic energy of a system of electrons directly from its electron density. They are used in electronic structure methods that lack direct access to orbitals, for example, orbital-free density functional theory (OFDFT) and certain embedding schemes. In this contribution, we introduce libKEDF, an accelerated library of modern KEDF implementations that emphasizes nonlocal KEDFs. We discuss implementation details and assess the performance of the KEDF implementations for large numbers of atoms. We show that using libKEDF, a single computing node or (GPU) accelerator can provide easy computational access to mesoscale chemical and materials science phenomena using OFDFT algorithms. © 2017 Wiley Periodicals, Inc.

Hydrodynamics of C-Start Escape Responses of Fish as Studied with Simple Physical Models.

One of the most-studied unsteady locomotor behaviors exhibited by fishes is the c-start escape response. Although the kinematics of these responses have been studied extensively and two well-defined kinematic stages have been documented, only a few studies have focused on hydrodynamic patterns generated by fishes executing escape behaviors. Previous work has shown that escape responses by bluegill sunfish generate three distinct vortex rings, each with central orthogonal jet flows, and here we extend this conclusion to two other species: stickleback and mosquitofish. Jet #1 is formed by the tail during Stage 1, and moves in the same direction as Stage-2 movement of the fish, thereby reducing final escape-velocity but also rotating the fish. Jet #2, in contrast, moves approximately opposite to the final direction of the fish's motion and contains the bulk of the total fluid-momentum powering the escape response. Jet #3 forms during Stage 2 in the mid-body region and moves in a direction approximately perpendicular to jets 1 and 2, across the direction of movement of the body. In this study, we used a mechanical controller to impulsively move passively flexible plastic panels of three different stiffnesses in heave, pitch, and heave + pitch motions to study the effects of stiffness on unsteady hydrodynamics of escape. We were able to produce kinematics very similar to those of fish c-starts and also to reproduce the 3-jet hydrodynamic pattern of the c-start using a panel of medium flexural stiffness and the combined heave + pitch motion. This medium-stiffness panel matched the measured stiffness of the near-tail region of fish bodies. This motion also produced positive power when the panel straightened during stage 2 of the escape response. More flexible and stiffer panels resulted in non-biological kinematics and patterns of flow for all motions. The use of simple flexible models with a mechanical controller and program of fish-like motion is a promising approach for studying unsteady behaviors of fish which can be difficult to manipulate experimentally in live animals.