A fundamental mechanism for carbon-film lubricity identified by means of ab initio molecular dynamics

Different hypotheses have been proposed to explain the mechanism for the extremely low friction coefficient of carbon coatings and its undesired dependence on air humidity. A decisive atomistic insight is still lacking because of the difficulties in monitoring what actually happens at the buried sliding interface. Here we perform large-scale ab initio molecular dynamics simulations of both undoped and silicon-doped carbon films sliding in the presence of water. We observe the tribologically-induced surface hydroxylation and subsequent formation of a thin film of water molecules bound to the OH-terminated surface by hydrogen bonds. The comparative analysis of silicon-incorporating and clean surfaces, suggests that this two-step process can be the key phenomenon to provide high slipperiness to the carbon coatings. The water layer is, in fact, expected to shelter the carbon surface from direct solid-on-solid contact and make any counter surface slide extremely easily on it. The present insight into the wettability of carbon-based films can be useful for designing new coatings for biomedical and energy-saving applications with environmental adaptability.Comment: 22 pages, 4 figures, 1 tabl

Insigths into the tribochemistry of silicon-doped carbon based films by ab initio analysis of water/surface interactions

Diamond and diamond-like carbon (DLC) are used as coating materials for numerous applications, ranging from biomedicine to tribology. Recently, it has been shown that the hydrophilicity of the carbon films can be enhanced by silicon doping, which highly improves their biocompatibility and frictional performances. Despite the relevance of these properties for applications, a microscopic understanding on the effects of silicon is still lacking. Here we apply ab initio calculations to study the interaction of water molecules with Si-incorporated C(001) surfaces. We find that the presence of Si dopants considerably increases the energy gain for water chemisorption and decreases the energy barrier for water dissociation by more than 50%. We provide a physical rational for the phenomenon by analysing the electronic charge displacements occuring upon adsorption. We also show that once hydroxylated, the surface is able to bind further water molecules much strongly than the clean surface via hydrogen-bond networks. This two-step process is consistent with and can explain the enhanced hydrophilic character observed in carbon-based films doped by silicon

Spatial regression-based transfer learning for prediction problems

Although spatial prediction is widely used for urban and environmental monitoring, its accuracy is often unsatisfactory if only a small number of samples are available in the study area. The objective of this study was to improve the prediction accuracy in such a case through transfer learning using larger samples obtained outside the study area. Our proposal is to pre-train latent spatial-dependent processes, which are difficult to transfer, and apply them as additional features in the subsequent transfer learning. The proposed method is designed to involve local spatial dependence and can be implemented easily. This spatial-regression-based transfer learning is expected to achieve a higher and more stable prediction accuracy than conventional learning, which does not explicitly consider local spatial dependence. The performance of the proposed method was examined using land price and crime predictions. These results suggest that the proposed method successfully improved the accuracy and stability of these spatial predictions

Scalable model selection for spatial additive mixed modeling: application to crime analysis

A rapid growth in spatial open datasets has led to a huge demand for regression approaches accommodating spatial and non-spatial effects in big data. Regression model selection is particularly important to stably estimate flexible regression models. However, conventional methods can be slow for large samples. Hence, we develop a fast and practical model-selection approach for spatial regression models, focusing on the selection of coefficient types that include constant, spatially varying, and non-spatially varying coefficients. A pre-processing approach, which replaces data matrices with small inner products through dimension reduction dramatically accelerates the computation speed of model selection. Numerical experiments show that our approach selects the model accurately and computationally efficiently, highlighting the importance of model selection in the spatial regression context. Then, the present approach is applied to open data to investigate local factors affecting crime in Japan. The results suggest that our approach is useful not only for selecting factors influencing crime risk but also for predicting crime events. This scalable model selection will be key to appropriately specifying flexible and large-scale spatial regression models in the era of big data. The developed model selection approach was implemented in the R package spmoran

Fast simulation for optical systems addressing the curse of dimensionality of multi-photons in quantum mechanics

Photons are an elementary particle of light, whose detailed understanding plays a key in unraveling the mysteries of quantum mechanics. However, its counter-intuitive quantum nature makes it challenging to gain insights into its dynamics, particularly in complex systems. Simulation is a promising tool to resolve this issue, but previous methods are limited by the curse of dimensionality, namely, that the number of bases increases exponentially in the number of photons. Here we mitigate this dimensionality scaling by focusing on optical system composed of linear-optical objects. We decompose the time evolutionary operator on multiple photons into a group of time evolution operators acting on a single photon. Since the dimension of a single-photon time evolution operator is exponentially smaller than that of a multi-photon one in the number of photons, the decomposition enables the multi-photon simulations to be performed at a much lower computational cost. We apply this method to basic single- and multi-photon phenomena, such as Hong-Ou-Mandel interference and violation of the Bell-CHSH inequality, and confirm that the calculated properties are quantitatively comparable to the experimental results. Furthermore, our method visualizes the spatial propagation of photons hence provides insights that aid experiment designs for quantum-enabled technologies.Comment: 17 pages, 6 figures, 1 tabl

Quantum topology optimization of ground structures using noisy intermediate-scale quantum devices

To arrive at some viable product design, product development processes frequently use numerical simulations and mathematical programming techniques. Topology optimization, in particular, is one of the most promising techniques for generating insightful design choices. Topology optimization problems reduce to an NP-hard combinatorial optimization problem, where the combination of the existence or absence of the material at some positions is optimized. In this study, we examine the usage of quantum computers as a potential solution to topology optimization problems. The proposed method consists of two variational quantum algorithms (VQAs): the first solves the state equilibrium equation for all conceivable material configurations, while the second amplifies the likelihood of an optimal configuration in quantum superposition using the first VQA's quantum state. Several experiments, including a real device experiment, show that the proposed method successfully obtained the optimal configurations. These findings suggest that quantum computers could be a potential tool for solving topology optimization problems and they open the window to the near-future product designs.Comment: 16 pages, 6 figure

Creation of crystal structure reproducing X-ray diffraction pattern without using database

When a sample's X-ray diffraction pattern (XRD) is measured, the corresponding crystal structure is usually determined by searching for similar XRD patterns in the database. However, if a similar XRD pattern is not found, it is tremendously laborious to identify the crystal structure even for experts. This case commonly happens when researchers develop novel and complex materials. In this study, we propose a crystal structure creation scheme that reproduces a given XRD pattern. We employed a combinatorial inverse design method using an evolutionary algorithm and crystal morphing (Evolv&Morph) supported by Bayesian optimization, which maximizes the similarity of the XRD patterns between target one and those of the created crystal structures. For sixteen different crystal structure systems with twelve simulated and four powder target XRD patterns, Evolv&Morph successfully created crystal structures with the same XRD pattern as the target (cosine similarity > 99% for the simulated ones and > 96% the experimentally-measured ones). Furthermore, the present method has merits in that it is an automated crystal structure creation scheme, not dependent on a database. We believe that Evolv&Morph can be applied not only to determine crystal structures but also to design materials for specific properties.Comment: 18 pages, 5 figures, 2 tables, submitted to npjC