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Reactive transport modeling of subsurface environments plays an important role in addressing critical problems of geochemical processes, such as dissolution and precipitation of minerals. Current transport models for porous media span various scales, ranging from pore-scale to continuum-scale. In this study, we established an upscaling method connecting pore-scale and continuum-scale models by employing a deep learning methodology of Convolutional Neural Networks (CNNs). We applied Darcy-Brinkmann-Stokes (DBS) method to simulate the fluid flow and reactive transport in pore-scale models, which would act as constituents of a continuum-scale model. The datasets of spatial pore distribution of subvolume samples were used as the input for the deep learning model, and the continuum (Darcy)-scale parameters such as permeability, effective surface area, and effective diffusion coefficient were figured out as outputs (i.e., labels). By applying the trained models of the subvolumes in the entire sample volume, we generated the initial field of porosity, permeability, effective diffusion coefficient, and effective surface area for continuum-scale simulation of a mineral dissolution problem. We took an acid dissolution case as an example to utilize the outcomes of trained deep learning models as input data in the continuum-scale simulation. This work presents a comprehensive upscaling workflow, as bridging the findings of microscale simulations to the continuum-scale simulations of a reactive transport problem. 

Switching of magnetization by spin–orbit torque in the (Ga,Mn)(As,P) film was studied with currents along ⟨100⟩ crystal directions and an in-plane magnetic field bias. This geometry allowed us to identify the presence of two independent spin–orbit-induced magnetic fields: the Rashba field and the Dresselhaus field. Specifically, we observe that when the in-plane bias field is along the current (I[Formula: see text]H bias ), switching is dominated by the Rashba field, while the Dresselhaus field dominates magnetization reversal when the bias field is perpendicular to the current (I ⊥ H bias ). In our experiments, the magnitudes of the Rashba and Dresselhaus fields were determined to be 2.0 and 7.5 Oe, respectively, at a current density of 8.0 × 10 5 A/cm 2 . 

Interlayer exchange coupling (IEC) has been intensively investigated in magnetic multilayers, owing to its potential for magnetic memory and logic device applications. Although IEC can be reliably obtained in metallic ferromagnetic multilayer systems by adjusting structural parameters, it is difficult to achieve gate control of IEC in metallic systems due to their large carrier densities. Here, we demonstrate that IEC can be reliably controlled in ferromagnetic semiconductor (FMS) trilayer structures by means of an external gate voltage. We show that, by designing a quantum-well-type trilayer structure based on (Ga,Mn)(As,P) FMSs and adapting the ionic liquid gating technique, the carrier density in the nonmagnetic spacer of the system can be modulated with gate voltages of only a few volts. Due to this capability, we are able to vary the strength of IEC by as much as 49% in the FMS trilayer. These results provide important insights into design of spintronic devices and their energy-efficient operation.