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Billion atom molecular dynamics (MD) using quantum-accurate machine-learning Spectral Neighbor Analysis Potential (SNAP) observed long-sought high pressure BC8 phase of carbon at extreme pressure (12 Mbar) and temperature (5,000 K). 24-hour, 4650 node production simulation on OLCF Summit demonstrated an unprecedented scaling and unmatched real-world performance of SNAP MD while sampling 1 nanosecond of physical time. Efficient implementation of SNAP force kernel in LAMMPS using the Kokkos CUDA backend on NVIDIA GPUs combined with excellent strong scaling (better than 97% parallel efficiency) enabled a peak computing rate of 50.0 PFLOPs (24.9% of theoretical peak) for a 20 billion atom MD simulation on the full Summit machine (27,900 GPUs). The peak MD performance of 6.21 Matom-steps/node-s is 22.9 times greater than a previous record for quantum-accurate MD. Near perfect weak scaling of SNAP MD highlights its excellent potential to advance the frontier of quantum-accurate MD to trillion atom simulations on upcoming exascale platforms.

The time scale associated with shock-induced detonation is a key property of energetic materials that remains poorly understood. Herein, we test aspects of one potential mechanism, the phonon up-pumping mechanism, where shock compression excites lattice phonon modes, transferring energy to intramolecular vibrations leading to chemical bond cleavage and reaction. Using ultrafast infrared pump–probe spectroscopy on pentaerythritol tetranitrate (PETN), we reveal sub-picosecond vibrational energy transfer (VET) from the photoexcited band at 1660 cm–1 into every other infrared-active mode in the probed frequency range 800–1800 cm–1. Energy transfer processes remain incomplete at 150 ps. Computational predictions from density functional theory are used in tandem to elucidate VET pathways in PETN.

A natural extension of the descriptors used in the Spectral Neighbor Analysis Potential (SNAP) method is derived to treat atomic interactions in chemically complex systems. Atomic environment descriptors within SNAP are obtained from a basis function expansion of the weighted density of neighboring atoms. This new formulation instead partitions the neighbor density into partial densities for each chemical element, thus leading to explicit multi-element descriptors. For $N_{elem}$ chemical elements, the number of descriptors increases as $\mathcal{O}(N_{elem}^3)$, while the computational cost of the force calculation as implemented in LAMMPS is limited to $\mathcal{O}(N_{elem}^2)$ and the favorable linear scaling in the number of atoms is retained. We demonstrate these chemically aware descriptors by producing an interatomic potential for indium phosphide capable of capturing high-energy defects that result from radiation damage cascades. This new explicit multi-element SNAP method reproduces the relaxed defect formation energies with substantially greater accuracy than weighted-density SNAP, while retaining accurate representation of the bulk indium phosphide properties., Comment: 11 pages, 4 figures

Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs (ML-IAPs) based on four local environment descriptors-atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the spectral neighbor analysis potential (SNAP) bispectrum components, and moment tensors-using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model and, consequently, computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.

We present a new quantum accurate Spectral Neighbor Analysis Potential (SNAP) machine-learning potential for simulating carbon under extreme conditions of dynamic compression (pressures up to 1 TPa and temperatures up to 10,000 K). The development of SNAP potential involves (1) the generation of the training database comprised of the consistent and meaningful set of first-principles DFT (Density Functional Theory) data for carbon materials at high pressure and temperature; (2) the robust and physically guided training of the SNAP parameters on first-principles data involving statistical data analysis; and (3) the validation of the SNAP potential in MD simulations of carbon at high PT conditions. The excellent performance of quadratic SNAP potential is demonstrated by simulating the radial distribution functions at high pressure-temperature conditions and melt curve of diamond, which were found in good agreement with DFT.

Erosion of the beryllium first wall material in tokamak reactors has been shown to result in transport and deposition on the tungsten divertor. Experimental studies of beryllium implantation in tungsten indicate that mixed W–Be intermetallic deposits can form, which have lower melting temperatures than tungsten and can trap tritium at higher rates. To better understand the formation and growth rate of these intermetallics, cumulative molecular dynamics (MD) simulations of both high and low energy beryllium deposition in tungsten were performed. In both cases, a W–Be mixed material layer (MML) emerged at the surface within several nanoseconds, either through energetic implantation or a thermally-activated exchange mechanism, respectively. While some ordering of the material into intermetallics occurred, fully ordered structures did not emerge from the deposition simulations. Targeted MD simulations of the MML to further study the rate of Be diffusion and intermetallic growth rates indicate that for both cases, the gradual re-structuring of the material into an ordered intermetallic layer is beyond accessible MD time scales(⩽1 μs). However, the rapid formation of the MML within nanoseconds indicates that beryllium deposition can influence other plasma species interactions at the surface and begin to alter the tungsten material properties. Therefore, beryllium deposition on the divertor surface, even in small amounts, is likely to cause significant changes in plasma-surface interactions and will need to be considered in future studies.

We explore the possibility of nonstatistical chemical reactions in condensed-phase energetic materials via reactive molecular dynamics (MD) simulations. We characterize the response of nitromethane [CH3NO2], HMX [cyclic (CH2-NNO2)4], and PETN [C-(CH2-ONO2)4] to different types of insults: electric fields of various frequencies and strengths and direct heating at various rates. We find that nonequilibrium states can be created for short time scales when energy input targets specific vibrations through the electric fields and that equilibration eventually occurs even while the insults remain present. Interestingly, for strong fields these relaxation time scales are comparable to those of the initial chemical decomposition of the molecules. NM decomposes predominantly via bimolecular reactions, and while insults targeting specific modes lead to strong nonequilibrium states, they do not affect the kinetics associated with decomposition. PETN decomposes via the unimolecular formation of NO2 and, quite interest...

The ability of high-entropy alloys to resist radiation damage is rooted in their compositional complexity and associated high configurational entropy. In addition, grain boundaries within all alloys serve as effective sinks for defects. Using atomistic modeling, we investigated defect–grain boundary interaction mechanisms near ordered and amorphous grain boundaries in pure nickel and in a model, quaternary, high-entropy alloy (FeCoCrNi). Our results demonstrate that a combination of compositional complexity with amorphization of the grain boundary leads to much more efficient recombination and annihilation mechanisms. Coupling these two microstructural features results in the lowest amount of residual damage, indicating that these effects compound to increase radiation tolerance. These observations are rooted in locally dependent defect migration barriers in the high-entropy alloy and the strong trapping at both ordered and amorphous grain boundaries.

The purpose of this short contribution is to report on the development of a Spectral Neighbor Analysis Potential (SNAP) for tungsten. We have focused on the characterization of elastic and defect properties of the pure material in order to support molecular dynamics simulations of plasma-facing materials in fusion reactors. A parallel genetic algorithm approach was used to efficiently search for fitting parameters optimized against a large number of objective functions. In addition, we have shown that this many-body tungsten potential can be used in conjunction with a simple helium pair potential to produce accurate defect formation energies for the W-He binary system.

We study the scattering of electromagnetic waves in anisotropic energetic materials. Nonlinear light-matter interactions in molecular crystals result in frequency-conversion and polarization changes. Applied electromagnetic fields of moderate intensity can induce these nonlinear effects without triggering chemical decomposition, offering a mechanism for non-ionizing identification of explosives. We use molecular dynamics simulations to compute such two-dimensional Raman spectra in the terahertz range for planar slabs made of PETN and ammonium nitrate. We discuss third-harmonic generation and polarization-conversion processes in such materials. These observed far-field spectral features of the reflected or transmitted light may serve as an alternative tool for stand-off explosive detection., Comment: 6 pages, 6 figures, LA-UR-15-27584

We use molecular dynamics simulations with the reactive potential ReaxFF to investigate the initial reactions and subsequent decomposition in the high-energy-density material α-HMX excited thermally and via electric fields at various frequencies. We focus on the role of insult type and strength on the energy increase for initial decomposition and onset of exothermic chemistry. We find both of these energies increase with the increasing rate of energy input and plateau as the processes become athermal for high loading rates. We also find that the energy increase required for exothermic reactions and, to a lesser extent, that for initial chemical reactions depend on the insult type. Decomposition can be induced with relatively weak insults if the appropriate modes are targeted but increasing anharmonicities during heating lead to fast energy transfer and equilibration between modes that limit the effect of loading type.