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Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures. However, the development of carbon active materials with improved physicochemical and electrochemical properties is generally carried out via time-consuming and cost-ineffective experimental processes. In this regard, machine-learning technology provides a data-driven approach to examine previously reported research works to find the critical features for developing ideal carbon materials for supercapacitors. Here, we report the design of a machine-learning-derived activation strategy that uses sodium amide and cross-linked polymer precursors to synthesize highly porous carbons (i.e., with specific surface areas > 4000 m²/g). Tuning the pore size and oxygen content of the carbonaceous materials, we report a highly porous carbon-base electrode with 0.7 mg/cm²of electrode mass loading that exhibits a high specific capacitance of 610 F/g in 1 M H₂SO₄. This result approaches the specific capacitance of a porous carbon electrode predicted by the machine learning approach. We also investigate the charge storage mechanism and electrolyte transport properties via step potential electrochemical spectroscopy and quasielastic neutron scattering measurements.

 

Abstract Second-harmonic Hall voltage (SHV) measurement method has been widely used to characterize the strengths of spin–orbit torques (SOTs) in heavy metal/ferromagnet thin films saturated in the single-domain regime. Here, we show that the magnetic anisotropy of a W/Pt/Co trilayer can be robustly tuned from in-plane to out-of-plane by varying W, Pt, or Co thicknesses. Moreover, in samples with easy-cone anisotropy, SHV measurements exhibit anomalous ‘humps’ in the multidomain regime accessed by applying a nearly out-of-plane external magnetic field. These hump features can only be explained as a result of the formation of Néel-type domain walls, efficiently driven by nevertheless small SOTs in this double heavy metal heterostructure with canceling spin Hall angles. 

Recent advancements in the field of two-dimensional (2D) materials have led to the discovery of a wide range of 2D materials with intriguing properties. Atomistic-scale simulation methods have played a key role in these discoveries. In this review, we provide an overview of the recent progress in ReaxFF force field developments and applications in modeling the following layered and nonlayered 2D materials: graphene, transition metal dichalcogenides, MXenes, hexagonal boron nitrides, groups III-, IV- and V-elemental materials, as well as the mixed dimensional van der Waals heterostructures. We further discuss knowledge gaps and challenges associated with synthesis and characterization of 2D materials. We close this review with an outlook addressing the challenges as well as plans regarding ReaxFF development and possible large-scale simulations, which should be helpful to guide experimental studies in a discovery of new materials and devices.