Micromagnetic simulations based on the stochastic Landau-Lifshitz-Gilbert equation are used to calculate dynamic magnetic hysteresis loops relevant to magnetic hyperthermia. With the goal to effectively simulate room-temperature loops for large iron-oxide-based systems at relatively slow sweep rates on the order of 1 Oe/ns or less, a previously derived renormalization group approach for coarse-graining (Grinstein and Koch, Phys. Rev. Lett. 20, 207201, 2003) is modified and applied to calculating loops for a magnetite nanorod. The nanorod modelled is the building block for larger nanoparticles that were employed in preclinical studies (Dennis et al., Nanotechnology 20, 395103, 2009). The scaling algorithm is shown to produce nearly identical loops over several decades in the model grain size. Sweep-rate scaling involving the Gilbert damping parameter is also demonstrated to allow orders of magnitude speed-up of the loop calculations. arXiv:1912.00310v1 [cond-mat.mtrl-sci] 1 Dec 2019

Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they help enhance the machining quality by offering comprehension of the inter-relationships between the laser processing parameters. On the other hand, experimental processing parameter optimization recommends a systematic, and consequently time-consuming, investigation over the available processing parameter space. An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters for finding proper parameter combinations to create the desired cuts on industrial-grade alumina ceramic with deep, smooth and defect-free patterns. Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, as well as the vertical distance of the scanner from the sample surface, are used for predicting the depth, top width, and bottom width of the engraved channels using ML models. Owing to the complex correlation between laser parameters, it is shown that Neural Networks (NN) are the most efficient in predicting the outputs. Equipped with an ML model that captures the interconnection between laser parameters and the engraved channel dimensions, one can predict the required input parameters to achieve a target channel geometry. This strategy significantly reduces the cost and effort of experimental laser machining during the development phase, without compromising accuracy or performance. The developed techniques can be applied to a wide range of ceramic laser machining processes.

Attaining optimum structural ceramic designs calls for an extensive search in a vast design space. Herein, the thermomechanical properties of interlocked ceramics are evaluated and an approach to assist their design under thermal shock loading is proposed. A combination of finite‐element (FE) and machine learning (ML) methods is used to simulate behaviors of systems and then to sweep the vast domain of input combinations to determine the best‐performing designs, respectively. First, FE modeling is done using a limited number of interlocking architectures with different design parameters via Comsol Multiphysics. The simulation data is used for training ML algorithms. Of the examined ML algorithms, Gaussian process regression (GPR), extreme gradient boosting (XGB), and neural networks (NN) more accurately predict the thermomechanical responses of the interlocking ceramics. After validation, the combination of FE and ML approaches is applied to thermal shielding and heat sink applications to find the optimal interlocked ceramics in terms of minimal out‐of‐plane deformation and maximal heat absorption, respectively. The results show the success of the approach in finding optimum designs in a space of more than 2 million cases. The striking success of the ML approach implies its promising potential for predicting physical properties of ceramics.

We extend a renormalization group-based (RG) coarse-graining method for micromagnetic simulations to include properly scaled magnetostatic interactions. We apply the method in simulations of dynamic hysteresis loops at clinically relevant sweep rates and at 310 K of iron oxide nanoparticles (NPs) of the kind that have been used in preclinical studies of magnetic hyperthermia. The coarse-graining method, along with a time scaling involving sweep rate and Gilbert damping parameter, allow us to span length scales from the unit cell to NPs approximately 50 nm in diameter with reasonable simulation times. For both NPs and the nanorods composing them, we report effective uniaxial anisotropy strengths and saturation magnetizations, which differ from those of the bulk materials magnetite and maghemite of which they are made, on account of the combined non-trivial effects of temperature, inter-rod exchange, magnetostatic interactions and the degree of orientational order within the nanorod composites. The effective parameters allow treating the NPs as single macrospins, and we find for the test case of calculating loops for two aligned NPs that using the dipole approximation is sufficient for distances beyond 1.5 times the NP diameter. We also present a study on relating integration time step to micromagnetic cell size, finding that the optimal time step size scales approximately linearly with cell volume.