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Shape memory materials have been playing an important role in a wide range of bioengineering applications. At the same time, recent developments of graphene-based nanostructures, such as nanoribbons, have demonstrated that, due to the unique properties of graphene, they can manifest superior electronic, thermal, mechanical, and optical characteristics ideally suited for their potential usage for the next generation of diagnostic devices, drug delivery systems, and other biomedical applications. One of the most intriguing parts of these new developments lies in the fact that certain types of such graphene nanoribbons can exhibit shape memory effects. In this paper, we apply machine learning tools to build an interatomic potential from DFT calculations for highly ordered graphene oxide nanoribbons, a material that had demonstrated shape memory effects with a recovery strain up to 14.5% for 2D layers. The graphene oxide layer can shrink to a metastable phase with lower constant lattice through the application of an electric field, and returns to the initial phase through an external mechanical force. The deformation leads to an electronic rearrangement and induces magnetization around the oxygen atoms. DFT calculations show no magnetization for sufficiently narrow nanoribbons, while the machine learning model can predict the suppression of the metastable phase for the same narrower nanoribbons. We can improve the prediction accuracy by analyzing only the evolution of the metastable phase, where no magnetization is found according to DFT calculations. The model developed here allows also us to study the evolution of the phases for wider nanoribbons, that would be computationally inaccessible through a pure DFT approach. Moreover, we extend our analysis to realistic systems that include vacancies and boron or nitrogen impurities at the oxygen atomic positions. Finally, we provide a brief overview of the current and potential applications of the materials exhibiting shape memory effects in bioengineering and biomedical fields, focusing on data-driven approaches with machine learning interatomic potentials.

A stable lithium‐metal electrode can enable the shift from the Li‐ion batteries to the next generation chemistries such as Li−S and Li−O2 with significant gains in the energy density and sustainability. This transition, however, is hindered by the dendrite formation, high chemical reactivity, and volume changes of the Li electrode. Although recent advancements in computational and experimental research have deepened our understanding of these issues, the primary obstacles to the commercialization of the lithium‐metal batteries (LMBs) still persist. To address these challenges, a synergistic approach that combines computational and experimental strategies shows great promise. In this regard, this paper reviews the current experimental and theoretical understanding of the lithium‐metal electrodes in view of the initiation and growth mechanisms of the lithium dendrites and interface instability. Leveraging the strengths of both approaches can offer a holistic insight into the LMB performance and guide the development of innovative designs for electrolytes and electrodes that can enhance the stability and performance of the LMBs. Lithium metal is a key component of the battery technologies beyond Li‐ion enabling very high values of energy density. However, the aging of Li‐metal batteries (LMBs) is too fast due to the morphological instability of Li and the growth of dendrites. The coupling of experiment and modeling is a promising strategy to accelerate the discovery and optimization of the materials and electrodes for durable LMBs which is the topic of this review paper.

The development of manufacturing process concerns precision, comprehensiveness, agileness, high efficiency and low cost. The numerical simulation has become an important method for process design and optimization. Physics-based modeling was proposed to promote simulations with a high accuracy. In this paper, three cases, on material properties, precise boundary conditions, and micro-scale physical models, have been discussed to demonstrate how physics-based modeling can improve manufacturing simulation. By using this method, manufacturing process can be modeled precisely and optimized for getting better performance.