Search

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. During this process, we quantitatively assessed structural properties, elastic properties, dimer potential energies, melting temperatures, phase stability, point defect formation energies, point defect migration energies, free surface energies, and generalized stacking fault (GSF) energies of Zr as predicted by our MLFFs. Unsurprisingly, model complexity has a positive correlation with prediction accuracy. We also find that the MLFFs wee able to predict the properties of out-of-sample configurations without directly including these specific configurations in the training dataset. Additionally, we generated 100 MLFFs of high complexity (1513 parameters each) that reached different local optima during training. Their predictions cluster around the benchmark DFT values, but subtle physical features such as the location of local minima on the GSFE surface are washed out by statistical noise.

Efficient and precise prediction of plasticity by data-driven models relies on appropriate data preparation and a well-designed model. Here we introduce an unsupervised machine learning-based data preparation method to maximize the trainability of crystal orientation evolution data during deformation. For Taylor model crystal plasticity data, the preconditioning procedure improves the test score of an artificial neural network from 0.831 to 0.999, while decreasing the training iterations by an order of magnitude. The efficacy of the approach was further improved with a recurrent neural network. Electron backscattered (EBSD) lab measurements of crystal rotation during rolling were compared with the results of the surrogate model, and despite error introduced by Taylor model simplifying assumptions, very reasonable agreement between the surrogate model and experiment was observed. Our method is foundational for further data-driven studies, enabling the efficient and precise prediction of texture evolution from experimental and simulated crystal plasticity results., Comment: 15 pages, 6 figures

Neutron irradiation tends to promote disorder in ordered alloys through the action of the thermal spikes that it generates, while simultaneously introducing point defects and defect clusters. As they migrate, these point defects will promote reordering of the alloys, acting against irradiation-induced disordering. In this study, classical molecular dynamics and a highly parallel accelerated sampling method are used to study the reordering kinetics of Ni$_3$Al under the diffusion of self-interstitial atoms (SIA). By monitoring the order parameter and potential energy from atomistic simulations, we show that the SIA acts as a reordering agent in Ni$_3$Al. A mean-field rate theory model of the interstitialcy-based reordering kinetics is introduced, which reproduces simulation data and predicts reordering at temperatures as low as 500 K.

The order-disorder transition in Ni-Al alloys under irradiation represents an interplay between various re-ordering processes and disordering due to thermal spikes generated by incident high energy particles. Typically, ordering in 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 Ni3Al at temperatures where vacancies are not effective ordering agents. A rate-theory model is presented, that quantitatively explains this behavior, based on parameters extracted from atomistic simulations. These calculations show that the V2He complex is an effective agent through its high stability and mobility. It is surmised that immiscible atoms may stabilize reordering agents in other materials undergoing driven processes, and preserve ordered phases at temperature where the driven processes would otherwise lead to disorder.

Production rates in long-term predictive radiation damage accumulation models are generally considered independent of the material's microstructure for reactor components. In this study, the effect of pre-existing microstructural elements on primary damage production in alpha-Zr -- and vice-versa -- is assessed by molecular dynamics (MD) simulations. a-type dislocation loops, c-component dislocation loops and a tilt grain boundary (GB) were considered. Primary damage production is reduced in the presence of all these microstructural elements, and clustering behavior is dependent on the microstructure. Collision cascades do not cause a-type loop growth or shrinkage, but they cause c-component loop shrinkage. Cascades in the presence of the GBs produce more vacancies than interstitials. This result, as well as other theoretical, MD and experimental evidence, confirm that vacancy loops will grow in the vacancy supersaturated environment near GBs. Distinct temperature-dependent growth regimes are identified. Also, MD reveals cascade-induced events where a-type vacancy loops are absorbed by GBs. Fe segregation at the loops inhibits this cascade-induced absorption.