Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review.

In the last few decades, the influence of machine learning has permeated many areas of science and technology, including the field of materials science. This toolkit of data driven methods accelerated the discovery and production of new materials by accurately predicting the complicated physical processes and mechanisms that are not fully described by existing materials theories. However, the availability of a growing number of increasingly complex machine learning models confronts us with the question of "which machine learning algorithm to employ". In this review, we provide a comprehensive review of common machine learning algorithms used for materials design, as well as a guideline for selecting the most appropriate model considering the nature of the design problem. To this end, we classify the material design problems into four categories of: (i) the training data set being sufficiently large to capture the trend of design space (interpolation problem), (ii) a vast design space that cannot be explored thoroughly with the initial training data set alone (extrapolation problem), (iii) multi-fidelity datasets (small accurate dataset and large approximate dataset), and (iv) only a small dataset available. The most successful machine learning-based surrogate models and design approaches will be discussed for each case along with pertinent literature. This review focuses mostly on the use of ML algorithms for the inverse design of complicated composite structures, a topic that has received a lot of attention recently with the rise of additive manufacturing.

Development and Utilization of a Rapid and Accurate Epidemic Investigation Support System for COVID-19.

OBJECTIVES: In this pandemic situation caused by a novel coronavirus disease in 2019 (COVID-19), an electronic support system that can rapidly and accurately perform epidemic investigations, is needed. It would systematically secure and analyze patients' data (who have been confirmed to have the infection), location information, and credit card usage. METHODS: The "Infectious Disease Prevention and Control Act" in South Korea, established a legal basis for the securement, handling procedure, and disclosure of information required for epidemic investigations. The Epidemic Investigation Support System (EISS) was developed as an application platform on the Smart City data platform. RESULTS: The EISS performed the function of inter-institutional communication which reduced the processing period of patients' data in comparison to other methods. This system automatically marked confirmed cases' tracking data on a map and hot-spot analysis which lead to the prediction of areas where people may be vulnerable to infection. CONCLUSION: The EISS was designed and implemented for use during an epidemic investigation to prevent the spread of an infectious disease, by specifically tracking confirmed cases of infection.

Design of a Broadband Solar Thermal Absorber Using a Deep Neural Network and Experimental Demonstration of Its Performance.

In using nanostructures to design solar thermal absorbers, computational methods, such as rigorous coupled-wave analysis and the finite-difference time-domain method, are often employed to simulate light-structure interactions in the solar spectrum. However, those methods require heavy computational resources and CPU time. In this study, using a state-of-the-art modeling technique, i.e., deep learning, we demonstrate significant reduction of computational costs during the optimization processes. To minimize the number of samples obtained by actual simulation, only regulated amounts are prepared and used as a data set to train the deep neural network (DNN) model. Convergence of the constructed DNN model is carefully examined. Moreover, several analyses utilizing an evolutionary algorithm, which require a remarkable number of performance calculations, are performed using the trained DNN model. We show that deep learning effectively reduces the actual simulation counts compared to the case of a design process without a neural network model. Finally, the proposed solar thermal absorber is fabricated and its absorption performance is characterized.

Bayesian inference of the flow resistivity of a sound absorber and the room's influence on the Sabine absorption coefficients.

A Bayesian analysis is applied to determine the flow resistivity of a porous sample and the influence of the test chamber based on measured Sabine absorption coefficient data. The Sabine absorption coefficient measured in a reverberation chamber according to ISO 354 is influenced by the test chamber significantly, whereas the flow resistivity is a rather reproducible material property, from which the absorptive characteristics can be calculated through reliable models. Using Sabine absorption coefficients measured in 13 European reverberation chambers, the maximum a posteriori and the uncertainty of the flow resistivity and the test chamber's influence are estimated. Inclusion of more than one chamber's absorption data helps the flow resistivity converge towards a reliable value with a standard deviation below 17%.

Respiratory effects of the hebei spirit oil spill on children in taean, Korea.

PURPOSE: The oil spill from the Heibei Spirit in December 2007 contaminated the Yellow Coast of South Korea. We evaluated the respiratory effects of that spill on children who lived along the Yellow Coast. METHODS: Of 662 children living in the area exposed to the oil spill, 436 (65.9%) were enrolled as subjects. All subjects completed a modified International Study of Asthma and Allergies in Childhood questionnaire. A health examination, including a skin prick test, pulmonary function test, and methacholine bronchial provocation test (MBPT), was administered. The children were assigned to two groups: those who lived close to the oil spill area and those who lived far from the oil spill area. RESULTS: The children who lived close to the oil spill area showed a significantly lower forced expiratory volume in one second (FEV1), an increased prevalence of 'asthma ever' (based on a questionnaire), and 'airway hyperresponsiveness' (based on the MBPT) than those who lived far from the oil spill area (FEV1; P=0.011, prevalence of 'asthma ever' based on a questionnaire; P=0.005, prevalence of 'airway hyperresponsiveness' based on the MBPT; P=0.001). The onset of wheezing after the oil spill was significantly higher in children who lived close to the oil spill area than in those who lived far from the oil spill area among the 'wheeze ever' group (P=0.002). In a multiple logistic regression analysis, male sex, family history of asthma, and residence near the oil spill area were significant risk factors for asthma (sex [male/female]: odds ratio [OR], 2.54; 95% confidence interval [CI], 1.31-4.91; family history of asthma [No/Yes]: OR, 3.77; 95% CI, 1.83-7.75; exposure group [low/high]; OR, 2.43; 95% CI, 1.27-4.65). CONCLUSIONS: This study suggests that exposure to an oil spill is a risk factor for asthma in children.