Nanocrystalline metals offer significant improvements in structural performance over conventional alloys. However, their performance is limited by grain boundary instability and limited ductility. Solute segregation has been proposed as a stabilization mechanism, however the solute atoms can embrittle grain boundaries and further degrade the toughness. In the present study, we confirm the embrittling effect of solute segregation in Pt–Au alloys. However, more importantly, we show that inhomogeneous chemical segregation to the grain boundary can lead to a new toughening mechanism termed compositional crack arrest. Energy dissipation is facilitated by the formation of nanocrack networks formed when cracks arrested at regions of the grain boundaries that were starved in the embrittling element. This mechanism, in concert with triple junction crack arrest, provides pathways to optimize both thermal stability and energy dissipation. A combination of in situ tensile deformation experiments and molecular dynamics simulations elucidate both the embrittling and toughening processes that can occur as a function of solute content.
Low-mobility twin grain boundaries dominate the microstructure of grain boundary-engineered materials and are critical to understanding their plastic deformation behaviour. The presence of solutes, such as hydrogen, has a profound effect on the thermodynamic stability of the grain boundaries. This work examines the case of a Σ3 grain boundary at inclinations from 0° ≤ Φ ≤ 90°. The angle Φ corresponds to the rotation of the Σ3 (1 1 1) < 1 1 0 > (coherent) into the Σ3 (1 1 2) < 1 1 0 > (lateral) twin boundary. To this end, atomistic models of inclined grain boundaries, utilising empirical potentials, are used to elucidate the finite-temperature boundary structure while grand canonical Monte Carlo models are applied to determine the degree of hydrogen segregation. In order to understand the boundary structure and segregation behaviour of hydrogen, the structural unit description of inclined twin grain boundaries is found to provide insight into explaining the observed variation of excess enthalpy and excess hydrogen concentration on inclination angle, but the explanatory power is limited by how the enthalpy of segregation is affected by hydrogen concentration. At higher concentrations, the grain boundaries undergo a defaceting transition. In order to develop a more complete mesoscale model of the interfacial behaviour, an analytical model of boundary energy and hydrogen segregation that relies on modelling the boundary as arrays of discrete 1/3 < 1 1 1 > disconnections is constructed. Lastly, the complex interaction of boundary reconstruction and concentration-dependent segregation behaviour exhibited by inclined twin grain boundaries limits the range of applicability of such an analytical model and illustrates the fundamental limitations for a structural unit model description of segregation in lower stacking fault energy materials.
We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected onto a basis of hyperspherical harmonics in four dimensions. The bispectrum components are the same bond-orientational order parameters employed by the GAP potential [1]. The SNAP potential, unlike GAP, assumes a linear relationship between atom energy and bispectrum components. The linear SNAP coefficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. We demonstrate that a previously unnoticed symmetry property can be exploited to reduce the computational cost of the force calculations by more than one order of magnitude. We present results for a SNAP potential for tantalum, showing that it accurately reproduces a range of commonly calculated properties of both the crystalline solid and the liquid phases. In addition, unlike simpler existing potentials, SNAP correctly predicts the energy barrier for screw dislocation migration in BCC tantalum.
This report summarizes the result of LDRD project 12-0395, titled "Automated Algorithms for Quantum-level Accuracy in Atomistic Simulations." During the course of this LDRD, we have developed an interatomic potential for solids and liquids called Spectral Neighbor Analysis Poten- tial (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected on to a basis of hyperspherical harmonics in four dimensions. The SNAP coef- ficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. Global optimization methods in the DAKOTA software package are used to seek out good choices of hyperparameters that define the overall structure of the SNAP potential. FitSnap.py, a Python-based software pack- age interfacing to both LAMMPS and DAKOTA is used to formulate the linear regression problem, solve it, and analyze the accuracy of the resultant SNAP potential. We describe a SNAP potential for tantalum that accurately reproduces a variety of solid and liquid properties. Most significantly, in contrast to existing tantalum potentials, SNAP correctly predicts the Peierls barrier for screw dislocation motion. We also present results from SNAP potentials generated for indium phosphide (InP) and silica (SiO 2 ). We describe efficient algorithms for calculating SNAP forces and energies in molecular dynamics simulations using massively parallel computers and advanced processor ar- chitectures. Finally, we briefly describe the MSM method for efficient calculation of electrostatic interactions on massively parallel computers.
As current experimental and simulation methods cannot determine the mobility of flat boundaries across the large misorientation phase space, we have developed a computational method for imposing an artificial driving force on boundaries. In a molecular dynamics simulation, this allows us to go beyond the inherent timescale restrictions of the technique and induce non-negligible motion in flat boundaries of arbitrary misorientation. For different series of symmetric boundaries, we find both expected and unexpected results. In general, mobility increases as the grain boundary plane deviates from (111), but high-coincidence and low-angle boundaries represent special cases. These results agree with and enrich experimental observations.
