Hajime Shimakawa 교수

Abstract Data-driven materials science has realized a new paradigm by integrating materials domain knowledge and machine-learning (ML) techniques. However, ML-based research has often overlooked the inherent limitation in predicting unknown data: extrapolative performance, especially when dealing with small-scale experimental datasets. Here, we present a comprehensive benchmark for assessing extrapolative performance across 12 organic molecular properties. Our large-scale benchmark reveals that

Surface charge accumulation on an insulating spacer is a critical issue that causes electric field distortion and flashover voltage reduction in DC-GIS. In the operating environment, high-voltage electrode is heated by Joule heat, and the surface charge accumulation is strongly affected by the temperature. This study reports the temperature dependence of surface charge accumulation on DC-GIS insulating spacer and its impact on electric field distortion. Surface charge density distribution is mea

This study focuses on charge transport in epoxy bulk, an insulating spacer material. We discuss the relationship between the space charge behavior and the external current determined by conduction and displacement currents. One-dimensional space charge behavior in epoxy is simulated using a bipolar charge transport model that considers charge trapping, de-trapping and recombination. The simulated results show that the charge accumulation in epoxy is successfully modeled by considering high-densi

Surface charges on an insulating epoxy spacer in DC-GIS under high DC electric field lead to decrease breakdown voltage on the spacer, but charge accumulation phenomena of insulators are not clear in detail. In this paper, surface charge distributions on the epoxy model spacer were measured under DC-GIS simulated environment during the DC voltage application for 10,000 hours and the short circuit for 2400 hours. The saturation tendency of charge accumulation in which only the homo-charges develo

Machine learning (ML) has facilitated property prediction for intricate materials by integrating materials and experimental features such as processing and measurement conditions. However, ML models designed for material properties have often disregarded a common issue of "leakage," resulting in an overestimation of model performance and a decrease in model transferability. This issue can arise from biases inherent in multiple data points obtained from the same experimental group. We provide a c

There have been numerous experimental efforts in developing SF6 alternatives. However, promising candidates have not been identified due to the tradeoff relationship among multiple requirements necessary for eco-friendly insulating gases. This study presents a computational molecular exploration and experimental verification for identifying potential SF6 alternatives. We propose machine learning models based on quantum mechanical insights to realize extrapolative prediction of gas properties. Th

In DC gas-insulated switchgear (DC-GIS), surface charge accumulation on an insulating spacer is regarded as a cause of electric field distortion and flashover voltage reduction. In this study, we measure surface charge distribution on a downsized DCGIS epoxy spacer by electrostatic probes and develop a simulation model of surface charge accumulation based on charge transport in the presence of deep traps on the spacer surface. A bipolar charge transport model is adapted to calculate the evolutio

Surface charge accumulation on insulating epoxy spacer in DC-GIS under high DC electric field decreases breakdown voltage on the spacer, but charge accumulation phenomena of insulators are not clear in detail. In this paper, the numerical simulation of electrical conduction in epoxy is conducted to discuss proposed charge transport model. The charge transport model which had developed to explain electrical conduction in polyethylene is modified to simulate the prior profiles of space charge dist

In DC gas-insulated switchgear (DC-GIS), temperature of high-voltage busbar rises due to Joule heat, and a temperature gradient is generated in an insulating spacer. The effect of temperature gradient on charge accumulation of the spacer needs to be clarified. In this study, surface charge distribution on a disk-type downsized epoxy spacer is measured under temperature gradient and compared with the measured results under uniform temperatures. The experiments are carried out in 0.1 MPa-abs SF <i

Sulfur hexafluoride (SF6) is a powerful insulating gas, yet its global warming potential (GWP) of 25,200 compels us to urgently seek eco-friendly alternatives. Various experiments and computations have been conducted to develop SF6 alternatives that fulfill requirements for dielectric breakdown strength, GWP, and boiling point. However, the efforts has been hindered by limited experimental results as well as inadequate computational performance to extrapolatively predict gas properties of unknow

Surface charge accumulation on an insulating spacer in DC-GIS under high DC electric field is a problem because it decreases a breakdown voltage on the spacer. However, charge accumulation phenomena of an insulator are not well known. In this study, surface charge distribution at 40°C and at R.T. were measured and simulated. Simulation is performed with the charge transport model which had been developed to calculate electrical conduction in polyethylene. The model modified to calculate interfac

Surface charge accumulation phenomena on insulating epoxy spacer are measured under DC-GIS simulated environment. Homo-charges are accumulated near the electrodes, and charge injection from the electrodes is assumed to be dominant in surface charge accumulation. The surface charge is saturated at a time constant of 1000 hours below 20°C, and the saturated charge amount tends to increase with room temperature rising.