Multiscale simulation of plasma flows using active learning.

Plasma flows encountered in high-energy-density experiments display features that differ from those of equilibrium systems. Nonequilibrium approaches such as kinetic theory (KT) capture many, if not all, of these phenomena. However, KT requires closure information, which can be computed from microscale simulations and communicated to KT. We present a concurrent heterogeneous multiscale approach that couples molecular dynamics (MD) with KT in the limit of near-equilibrium flows. To reduce the cost of gathering information from MD, we use active learning to train neural networks on MD data obtained by randomly sampling a small subset of the parameter space. We apply this method to a plasma interfacial mixing problem relevant to warm dense matter, showing considerable computational gains when compared with the full kinetic-MD approach. We find that our approach enables the probing of Coulomb coupling physics across a broad range of temperatures and densities that are inaccessible with current theoretical models.

An informatics guided classification of miscible and immiscible binary alloy systems.

The classification of miscible and immiscible systems of binary alloys plays a critical role in the design of multicomponent alloys. By mining data from hundreds of experimental phase diagrams, and thousands of thermodynamic data sets from experiments and high-throughput first-principles (HTFP) calculations, we have obtained a comprehensive classification of alloying behavior for 813 binary alloy systems consisting of transition and lanthanide metals. Among several physics-based descriptors, the slightly modified Pettifor chemical scale provides a unique two-dimensional map that divides the miscible and immiscible systems into distinctly clustered regions. Based on an artificial neural network algorithm and elemental similarity, the miscibility of the unknown systems is further predicted and a complete miscibility map is thus obtained. Impressively, the classification by the miscibility map yields a robust validation on the capability of the well-known Miedema's theory (95% agreement) and shows good agreement with the HTFP method (90% agreement). Our results demonstrate that a state-of-the-art physics-guided data mining can provide an efficient pathway for knowledge discovery in the next generation of materials design.

Efficient parallel linear scaling construction of the density matrix for Born-Oppenheimer molecular dynamics.

We present an algorithm for the calculation of the density matrix that for insulators scales linearly with system size and parallelizes efficiently on multicore, shared memory platforms with small and controllable numerical errors. The algorithm is based on an implementation of the second-order spectral projection (SP2) algorithm [ Niklasson, A. M. N. Phys. Rev. B 2002 , 66 , 155115 ] in sparse matrix algebra with the ELLPACK-R data format. We illustrate the performance of the algorithm within self-consistent tight binding theory by total energy calculations of gas phase poly(ethylene) molecules and periodic liquid water systems containing up to 15,000 atoms on up to 16 CPU cores. We consider algorithm-specific performance aspects, such as local vs nonlocal memory access and the degree of matrix sparsity. Comparisons to sparse matrix algebra implementations using off-the-shelf libraries on multicore CPUs, graphics processing units (GPUs), and the Intel many integrated core (MIC) architecture are also presented. The accuracy and stability of the algorithm are illustrated with long duration Born-Oppenheimer molecular dynamics simulations of 1000 water molecules and a 303 atom Trp cage protein solvated by 2682 water molecules.

Dynamical and transport properties of liquid gallium at high pressures.

Quantum molecular dynamics (QMD) simulations are used to calculate the equation of state, structure, and transport properties of liquid gallium along the principal shock Hugoniot. The calculated Hugoniot is in very good agreement with experimental data up to a pressure of 150 GPa as well as with our earlier classical molecular dynamics calculations using a modified embedded atom method (MEAM) potential. The self-diffusion and viscosity calculated using QMD agree with experimental measurements better than the MEAM results, which we attribute to capturing the complexity of the electronic structure at elevated temperatures. Calculations of the DC conductivity were performed around the Hugoniot. Above a density of 7.5 g/cm(3), the temperature increases rapidly along the Hugoniot, and the optical conductivity decreases, indicating simple liquid metal behavior.

Use of the Richtmyer-Meshkov instability to infer yield stress at high-energy densities.

We use the Richtmyer-Meshkov instability (RMI) at a metal-gas interface to infer the metal's yield stress (Y) under shock loading and release. We first model how Y stabilizes the RMI using hydrodynamics simulations with a perfectly plastic constitutive relation for copper (Cu). The model is then tested with molecular dynamics (MD) of crystalline Cu by comparing the inferred Y from RMI simulations with direct stress-strain calculations, both with MD at the same conditions. Finally, new RMI experiments with solid Cu validate our simulation-based model and infer Y~0.47 GPa for a 36 GPa shock.

Direct observation of the alpha-epsilon transition in shock-compressed iron via nanosecond x-ray diffraction.

In situ x-ray diffraction studies of iron under shock conditions confirm unambiguously a phase change from the bcc (alpha) to hcp (epsilon) structure. Previous identification of this transition in shock-loaded iron has been inferred from the correlation between shock-wave-profile analyses and static high-pressure x-ray measurements. This correlation is intrinsically limited because dynamic loading can markedly affect the structural modifications of solids. The in situ measurements are consistent with a uniaxial collapse along the [001] direction and shuffling of alternate (110) planes of atoms, and are in good agreement with large-scale nonequilibrium molecular dynamics simulations.

Uniaxial Hugoniostat: a method for atomistic simulations of shocked materials.

An new equilibrium molecular-dynamics method (the uniaxial Hugoniostat) is proposed to study the energetics and deformation structures in shocked crystals. This method agrees well with nonequilibrium molecular-dynamics simulations used to study shock-wave propagation in solids and liquids.