Toward Machine-Learning-Accelerated Design of All-Dielectric Magnetophotonic Nanostructures.

All-dielectric magnetophotonic nanostructures are promising for integrated nanophotonic devices with high resolution and sensitivity, but their design requires computationally demanding electromagnetic simulations evaluated through trial and error. In this paper, we propose a machine-learning approach to accelerate the design of these nanostructures. Using a data set of 12 170 samples containing four geometric parameters of the nanostructure and the incidence wavelength, trained neural network and polynomial regression algorithms were capable of predicting the amplitude of the transverse magneto-optical Kerr effect (TMOKE) within a time frame of 10-3 s and mean square error below 4.2%. With this approach, one can readily identify nanostructures suitable for sensing at ultralow analyte concentrations in aqueous solutions. As a proof of principle, we used the machine-learning models to determine the sensitivity (S = |Δθres/Δna|) of a nanophotonic grating, which is competitive with state-of-the-art systems and exhibits a figure of merit of 672 RIU-1. Furthermore, researchers can use the predictions of TMOKE peaks generated by the algorithms to assess the suitability for experimental setups, adding a layer of utility to the machine-learning methodology.

Large-Language-Model-Based AI Agent for Organic Semiconductor Device Research.

Large language models (LLMs) have attracted widespread attention recently, however, their application in specialized scientific fields still requires deep adaptation. Here, an artificial intelligence (AI) agent for organic field-effect transistors (OFETs) is designed by integrating the generative pre-trained transformer 4 (GPT-4) model with well-trained machine learning (ML) algorithms. It can efficiently extract the experimental parameters of OFETs from scientific literature and reshape them into a structured database, achieving precision and recall rates both exceeding 92%. Combined with well-trained ML models, this AI agent can further provide targeted guidance and suggestions for device design. With prompt engineering and human-in-loop strategies, the agent extracts sufficient information of 709 OFETs from 277 research articles across different publishers and gathers them into a standardized database containing more than 10 000 device parameters. Using this database, a ML model based on Extreme Gradient Boosting is trained for device performance judgment. Combined with the interpretation of the high-precision model, the agent has provided a feasible optimization scheme that has tripled the charge transport properties of 2,6-diphenyldithieno[3,2-b:2',3'-d]thiophene OFETs. This work is an effective practice of LLMs in the field of organic optoelectronic devices and expands the research paradigm of organic optoelectronic materials and devices.

Data-Driven Tunnel Oxide Passivated Contact Solar Cell Performance Analysis Using Machine Learning.

Tunnel oxide passivated contacts (TOPCon) have gained interest as a way to increase the energy conversion efficiency of silicon solar cells, and the International Technology Roadmap of Photovoltaics forecasts TOPCon to become an important technology despite a few remaining challenges. To review the recent development of TOPCon cells, this work has compiled a dataset of all device data found in current literature, which sums up to 405 devices from 131 papers. This may seem like a surprisingly small number of cells given the recent interest in the TOPCon architecture, but it illustrates a problem of data dissemination in the field. Notwithstanding the limited number of cells, there is a great diversity in cell manufacturing procedures, and this work observes a gradual increase in performance indicating that the field has not yet converged on a set of best practices. By analyzing the data using statistical methods and machine learning (ML) algorithms, this work is able to reinforces some commonly held hypotheses related to the performance differences between different device architectures. This work also identifies a few more unintuitive feature combinations that would be of interest for further experimentally studies. This work also aims to inspire improvements in data management and dissemination within the TOPCon community.

Accelerated current-driven multi-objective topology optimization for compact ultrawide-band MIMO antenna design.

In this work, an accelerated current-driven multi-objective topology optimization method is proposed for compact high-isolation ultrawide-band (UWB) multiple input multiple output (MIMO) antenna design. The proposed method takes the current Pareto solutions as the centers to perform fuzzy clustering (FC) and then drives the evolution of individuals in each cluster according to the current distribution characteristics of the clustering centers. Individuals in each cluster purposefully generate antennas with expected performance, thereby accelerating the process of optimization design. The proposed method uses about half the computational cost of traditional algorithms to design a compact (20 × 18 mm2) and high-isolation (>20 dB) UWB MIMO antenna. The proposed method provides a new way for accelerating multi-objective topology optimization with a high design degree of freedom. Also, the optimized antenna is a competitive candidate solution for UWB applications.

Modular composite building in urgent emergency engineering projects: A case study of accelerated design and construction of Wuhan Thunder God Mountain/Leishenshan hospital to COVID-19 pandemic.

Wuhan Leishenshan/Leishenshan ("Leishenshan" for short) hospital is a makeshift emergency hospital for treating patients diagnosed with the novel coronavirus-infected pneumonia (NCIP). Engineering construction uses modular composite building finished products to the greatest extent, which reduces the workload of field operations and saves a lot of time. The building information model (BIM) technology assists in design and construction work to meet rapid construction requirements. Besides, based on the unmanned aerial vehicles (UAVs) data analysis and application platform, digitization and intelligence in engineering construction are improved. Simultaneously, on-site construction and overall hoisting were carried out to achieve maximum efficiency. This article aims to take the construction of Leishenshan Hospital as an example to illustrate how to adopt BIM technology and other high-tech technology such as big data, artificial intelligence, drones, and 5G for the fast construction of the fabricated steel structure systems in emergency engineering projects.

The development of children's inhibition: does parenting matter?

Whereas a large body of research has investigated the maturation of inhibition in relation to the prefrontal cortex, far less research has been devoted to environmental factors that could contribute to inhibition improvement. The aim of the current study was to test whether and to what extent parenting matters for inhibition development from 2 to 8years of age. Data were collected from 421 families, with 348 mother-child dyads and 342 father-child dyads participating. Children's inhibition capacities and parenting behaviors were assessed in a three-wave longitudinal data collection. The main analyses examined the impact of parenting on the development of children's inhibition capacities. They were conducted using a multilevel modeling (MLM) framework. The results lead to the conclusion that both mothers and fathers contribute through their child-rearing behavior to their children's executive functioning, even when controlling for age-related improvement (maturation) and important covariates such as gender, verbal IQ, and place of enrollment. More significant relations between children's inhibition development and parenting were displayed for mothers than for fathers. More precisely, parenting behaviors that involve higher monitoring, lower discipline, inconsistency and negative controlling, and a positive parenting style are associated with good development of inhibition capacities in children.