Extreme Metastability of Diamond and its Transformation to the BC8 Post-Diamond Phase of Carbon.

Diamond possesses exceptional physical properties due to its remarkably strong carbon-carbon bonding, leading to significant resilience to structural transformations at very high pressures and temperatures. Despite several experimental attempts, synthesis and recovery of the theoretically predicted post-diamond BC8 phase remains elusive. Through quantum-accurate multimillion atom molecular dynamics (MD) simulations, we have uncovered the extreme metastability of diamond at very high pressures, significantly exceeding its range of thermodynamic stability. We predict the post-diamond BC8 phase to be experimentally accessible only within a narrow high pressure-temperature region of the carbon phase diagram. The diamond to BC8 transformation proceeds through premelting followed by BC8 nucleation and growth in the metastable carbon liquid. We propose a double-shock compression pathway for BC8 synthesis, which is currently being explored in experiments at the National Ignition Facility.

The Value of Character-Based Judgement in the Professional Domain.

Dimensions of character are often overlooked in professional practice at the expense of the development of technical competence and operational efficiency. Drawing on philosophical accounts of virtue ethics and positive psychology, the present work attempts to elevate the role of 'good' character in the professional domain. A 'good' professional is ideally one that exemplifies dimensions of character informed by sound judgement. A total of 2340 professionals, from five discrete professions, were profiled based on their valuation of qualities pertaining to character and judgement. Profile differences were subsequently examined in the self-reported experience of professional purpose towards a wider societal 'good'. Analysis of covariance, controlling for stage of career, revealed that professionals valuing character reported higher professional purpose than those overweighting the importance of judgement or valuing neither character nor judgement, F(3, 2054) = 7.92, p < .001. No differences were found between the two groups valuing character, irrespective of whether judgement was valued simultaneously. This profiling analysis of entry-level and in-service professionals, based on their holistic character composition, paves the way for fresh philosophical discussion regarding what constitutes a 'good' professional and the interplay between character and judgement. The empirical findings may be of substantive value in helping to recognise how the dimensions of character and judgement may impact upon practitioners' professional purpose.

Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys.

We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the posterior variance of the MFGP, our framework naturally enables uncertainty quantification, providing estimates of confidence in the predictions. We used density functional theory as high-fidelity prediction, while a ML interatomic potential is used as low-fidelity prediction. Practical materials' design efficiency is demonstrated by reproducing the ternary composition dependence of a quantity of interest (bulk modulus) across the full aluminum-niobium-titanium ternary random alloy composition space. The MFGP is then coupled to a Bayesian optimization procedure, and the computational efficiency of this approach is demonstrated by performing an on-the-fly search for the global optimum of bulk modulus in the ternary composition space. The framework presented in this manuscript is the first application of MFGP to atomistic materials simulations fusing predictions between density functional theory and classical interatomic potential calculations.

Performance and Cost Assessment of Machine Learning Interatomic Potentials.

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.

Water desorption from rapidly-heated metal oxide surfaces-first principles, molecular dynamics, and the Temkin isotherm.

Quantitative understanding and control of water and impurity desorption from steel surfaces are crucial for high-voltage, pulsed power, vacuum technology, catalysis, and environmental applications. We apply a suite of modeling techniques, ranging from electronic density functional theory, to classical molecular dynamics (MD) and grand canonical Monte Carlo (GCMC) methods to study the thermodynamics and kinetics of fast water desorption from different surfaces of hematite Fe2O3 and Cr2O3. Water binding energies on chromium oxide are found to be higher than iron oxide at zero temperature. MD simulations are conducted on Fe2O3 surfaces using thermodynamically consistent initial water inventory deduced with GCMC. The resulting time- and temperature-dependent desorption profiles on the Fe2O3 [Formula: see text] surfaces show multi-water cooperative behavior which cannot be deduced from zero temperature predictions, but which are in reasonable agreement with simple Temkin isotherm model estimates if finite temperature effects are incorporated into the Temkin binding energy parameter. Qualitatively different desorption behaviors associated with the [Formula: see text] and [Formula: see text] facets are discussed.

Extending the accuracy of the SNAP interatomic potential form.

The Spectral Neighbor Analysis Potential (SNAP) is a classical interatomic potential that expresses the energy of each atom as a linear function of selected bispectrum components of the neighbor atoms. An extension of the SNAP form is proposed that includes quadratic terms in the bispectrum components. The extension is shown to provide a large increase in accuracy relative to the linear form, while incurring only a modest increase in computational cost. The mathematical structure of the quadratic SNAP form is similar to the embedded atom method (EAM), with the SNAP bispectrum components serving as counterparts to the two-body density functions in EAM. The effectiveness of the new form is demonstrated using an extensive set of training data for tantalum structures. Similar to artificial neural network potentials, the quadratic SNAP form requires substantially more training data in order to prevent overfitting. The quality of this new potential form is measured through a robust cross-validation analysis.

Development of a ReaxFF reactive force field for ammonium nitrate and application to shock compression and thermal decomposition.

We have developed a new ReaxFF reactive force field parametrization for ammonium nitrate. Starting with an existing nitramine/TATB ReaxFF parametrization, we optimized it to reproduce electronic structure calculations for dissociation barriers, heats of formation, and crystal structure properties of ammonium nitrate phases. We have used it to predict the isothermal pressure-volume curve and the unreacted principal Hugoniot states. The predicted isothermal pressure-volume curve for phase IV solid ammonium nitrate agreed with electronic structure calculations and experimental data within 10% error for the considered range of compression. The predicted unreacted principal Hugoniot states were approximately 17% stiffer than experimental measurements. We then simulated thermal decomposition during heating to 2500 K. Thermal decomposition pathways agreed with experimental findings.

Atomistic simulation of orientation dependence in shock-induced initiation of pentaerythritol tetranitrate.

The dependence of the reaction initiation mechanism of pentaerythritol tetranitrate (PETN) on shock orientation and shock strength is investigated with molecular dynamics simulations using a reactive force field and the multiscale shock technique. In the simulations, a single crystal of PETN is shocked along the [110], [001], and [100] orientations with shock velocities in the range 3-10 km/s. Reactions occur with shock velocities of 6 km/s or stronger, and reactions initiate through the dissociation of nitro and nitrate groups from the PETN molecules. The most sensitive orientation is [110], while [100] is the most insensitive. For the [001] orientation, PETN decomposition via nitro group dissociation is the dominant reaction initiation mechanism, while for the [110] and [100] orientations the decomposition is via mixed nitro and nitrate group dissociation. For shock along the [001] orientation, we find that CO-NO(2) bonds initially acquire more kinetic energy, facilitating nitro dissociation. For the other two orientations, C-ONO(2) bonds acquire more kinetic energy, facilitating nitrate group dissociation.

Molten salt eutectics from atomistic simulations.

Despite their importance for solar thermal power applications, phase-diagrams of molten salt mixture heat transfer fluids (HTFs) are not readily accessible from first principles. We present a molecular dynamics scheme general enough to identify eutectics of any HTF candidate mixture. The eutectic mixture and temperature are located using the liquid mixture free energy and the pure component solid-liquid free energy differences. The liquid mixture free energy is obtained using thermodynamic integration over particle identity transmutations sampled with molecular dynamics at a single temperature. Drawbacks of conventional phase diagram mapping methodologies are avoided by not considering solid mixtures, thereby evading expensive computations of solid phase free energies. Numerical results for binary and ternary mixtures of alkali nitrates agree well with experimental measurements.

Efficient hybrid evolutionary optimization of interatomic potential models.

The lack of adequately predictive atomistic empirical models precludes meaningful simulations for many materials systems. We describe advances in the development of a hybrid, population based optimization strategy intended for the automated development of material specific interatomic potentials. We compare two strategies for parallel genetic programming and show that the Hierarchical Fair Competition algorithm produces better results in terms of transferability, despite a lower training set accuracy. We evaluate the use of hybrid local search and several fitness models using system energies and/or particle forces. We demonstrate a drastic reduction in the computation time with the use of a correlation-based fitness statistic. We show that the problem difficulty increases with the number of atoms present in the systems used for model development and demonstrate that vectorization can help to address this issue. Finally, we show that with the use of this method, we are able to "rediscover" the exact model for simple known two- and three-body interatomic potentials using only the system energies and particle forces from the supplied atomic configurations.

Reactive molecular dynamics simulations of shock through a single crystal of pentaerythritol tetranitrate.

Large-scale molecular dynamics simulations and the reactive force field ReaxFF were used to study shock-induced initiation in crystalline pentaerythritol tetranitrate (PETN). In the calculations, a PETN single crystal was impacted against a wall, driving a shockwave back through the crystal in the [100] direction. Two impact speeds (4 and 3 km/s) were used to compare strong and moderate shock behavior. The primary difference between the two shock strengths is the time required to exhibit the same qualitative behaviors with the lower impact speed lagging behind the faster impact speed. For both systems, the shock velocity exhibits an initial deceleration due to onset of endothermic reactions followed by acceleration due to the onset of exothermic reactions. At long times, the shock velocity reaches a steady value. After the initial deceleration period, peaks are observed in the profiles of the density and axial stress with the strongly shocked system having sharp peaks while the weakly shocked system developed broad peaks due to the slower shock velocity acceleration. The dominant initiation reactions in both systems lead to the formation of NO(2) with lesser quantities of NO(3) and formaldehyde also produced.

General formulation of pressure and stress tensor for arbitrary many-body interaction potentials under periodic boundary conditions.

Three distinct forms are derived for the force virial contribution to the pressure and stress tensor of a collection of atoms interacting under periodic boundary conditions. All three forms are written in terms of forces acting on atoms, and so are valid for arbitrary many-body interatomic potentials. All three forms are mathematically equivalent. In the special case of atoms interacting with pair potentials, they reduce to previously published forms. (i) The atom-cell form is similar to the standard expression for the virial for a finite nonperiodic system, but with an explicit correction for interactions with periodic images. (ii) The atom form is particularly suited to implementation in modern molecular dynamics simulation codes using spatial decomposition parallel algorithms. (iii) The group form of the virial allows the contributions to the virial to be assigned to individual atoms.

A constant-time kinetic Monte Carlo algorithm for simulation of large biochemical reaction networks.

The time evolution of species concentrations in biochemical reaction networks is often modeled using the stochastic simulation algorithm (SSA) [Gillespie, J. Phys. Chem. 81, 2340 (1977)]. The computational cost of the original SSA scaled linearly with the number of reactions in the network. Gibson and Bruck developed a logarithmic scaling version of the SSA which uses a priority queue or binary tree for more efficient reaction selection [Gibson and Bruck, J. Phys. Chem. A 104, 1876 (2000)]. More generally, this problem is one of dynamic discrete random variate generation which finds many uses in kinetic Monte Carlo and discrete event simulation. We present here a constant-time algorithm, whose cost is independent of the number of reactions, enabled by a slightly more complex underlying data structure. While applicable to kinetic Monte Carlo simulations in general, we describe the algorithm in the context of biochemical simulations and demonstrate its competitive performance on small- and medium-size networks, as well as its superior constant-time performance on very large networks, which are becoming necessary to represent the increasing complexity of biochemical data for pathways that mediate cell function.