A unified moment tensor potential for silicon, oxygen, and silica.

Si and its oxides have been extensively explored in theoretical research due to their technological importance. Simultaneously describing interatomic interactions within both Si and SiO without the use of ab initio methods is considered challenging, given the charge transfers involved. Herein, this challenge is overcome by developing a unified machine learning interatomic potentials describing the Si/SiO/O system, based on the moment tensor potential (MTP) framework.

A Set of Moment Tensor Potentials for Zirconium with Increasing Complexity.

Machine learning force fields (MLFFs) are an increasingly popular choice for atomistic simulations due to their high fidelity and improvable nature. Here we propose a hybrid small-cell approach that combines attributes of both offline and active learning to systematically expand a quantum-mechanical (QM) database while constructing MLFFs with increasing model complexity. Our MLFFs employ the moment tensor potential formalism.

Accelerated kinetic Monte Carlo: A case study; vacancy and dumbbell interstitial diffusion traps in concentrated solid solution alloys.

Vacancy and self-interstitial atomic diffusion coefficients in concentrated solid solution alloys can have a non-monotonic concentration dependence. Here, the kinetics of monovacancies and ⟨100⟩ dumbbell interstitials in Ni-Fe alloys are assessed using lattice kinetic Monte Carlo (kMC). The non-monotonicity is associated with superbasins, which impels using accelerated kMC methods.

Automated Detection of Helium Bubbles in Irradiated X-750.

Imaging nanoscale features using transmission electron microscopy is key to predicting and assessing the mechanical behavior of structural materials in nuclear reactors. Analyzing these micrographs is often a tedious and labour intensive manual process. It is a prime candidate for automation.

Effect of He on the Order-Disorder Transition in Ni_{3}Al under Irradiation.

The order-disorder transition in Ni-Al alloys under irradiation represents an interplay between various reordering processes and disordering due to thermal spikes generated by incident high energy particles. Typically, ordering is enabled by diffusion of thermally generated vacancies, and can only take place at temperatures where they are mobile and in sufficiently high concentration. Here, in situ transmission electron micrographs reveal that the presence of He-usually considered to be a deleterious immiscible atom in this material-promotes reordering in Ni_{3}Al at temperatures where vacancies are not effective ordering agents.

Atomistic and mesoscale simulation of sodium and potassium adsorption in cement paste.

An atomistic and mesoscopic assessment of the effect of alkali uptake in cement paste is performed. Semi-grand canonical Monte Carlo simulations indicate that Na and K not only adsorb at the pore surface of calcium silicate hydrates (C-S-H) but also adsorb in the C-S-H hydrated interlayer up to concentrations of the order of 0.05 and 0.

Probing Potential Energy Surface Exploration Strategies for Complex Systems.

The efficiency of minimum-energy configuration searching algorithms is closely linked to the energy landscape structure of complex systems, yet these algorithms often include a number of steps of which the effect is not always clear. Decoupling these steps and their impacts can allow us to better understand both their role and the nature of complex energy landscape. Here, we consider a family of minimum-energy algorithms based, directly or indirectly, on the well-known Bell-Evans-Polanyi (BEP) principle.

Replenish and relax: explaining logarithmic annealing in ion-implanted c-Si.

We study ion-damaged crystalline silicon by combining nanocalorimetric experiments with an off-lattice kinetic Monte Carlo simulation to identify the atomistic mechanisms responsible for the structural relaxation over long time scales. We relate the logarithmic relaxation, observed in a number of disordered systems, with heat-release measurements. The microscopic mechanism associated with this logarithmic relaxation can be described as a two-step replenish and relax process.

Kinetic activation-relaxation technique.

We present a detailed description of the kinetic activation-relaxation technique (k-ART), an off-lattice, self-learning kinetic Monte Carlo (KMC) algorithm with on-the-fly event search. Combining a topological classification for local environments and event generation with ART nouveau, an efficient unbiased sampling method for finding transition states, k-ART can be applied to complex materials with atoms in off-lattice positions or with elastic deformations that cannot be handled with standard KMC approaches. In addition to presenting the various elements of the algorithm, we demonstrate the general character of k-ART by applying the algorithm to three challenging systems: self-defect annihilation in c-Si (crystalline silicon), self-interstitial diffusion in Fe, and structural relaxation in a-Si (amorphous silicon).

Optimized energy landscape exploration using the ab initio based activation-relaxation technique.

Unbiased open-ended methods for finding transition states are powerful tools to understand diffusion and relaxation mechanisms associated with defect diffusion, growth processes, and catalysis. They have been little used, however, in conjunction with ab initio packages as these algorithms demanded large computational effort to generate even a single event. Here, we revisit the activation-relaxation technique (ART nouveau) and introduce a two-step convergence to the saddle point, combining the previously used Lanczós algorithm with the direct inversion in interactive subspace scheme.