北海道大学 · Materials Science
키이스케 타카하시 교수의 연구실은 촉매 정보학과 재료 정보학을 중심으로, 데이터 기반의 재료 및 촉매 설계를 선도하고 있습니다. 고속 스크리닝을 통한 대량 촉매 데이터셋 구축과 머신러닝 기반 예측 모델 개발을 통해 태양광 변환 및 메탄 산화 촉매 등 친환경 에너지 소재의 발굴에 주력하고 있습니다. 특히, 물리적 특성과 전자구조 간의 관계를 해석하고, 신규 퍼보스카이트 등 태양전지 소재의 잠재적 후보를 정량적으로 예측하는 데에 초점을 맞추고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
The presence of a dataset that covers a parametric space of materials and process conditions in a process-consistent manner is essential for the realization of catalyst informatics. Here, an important piece of progress is demonstrated for the oxidative coupling of methane. A high-throughput screening instrument is developed for enabling an automatic performance evaluation of 20 catalysts in 216 reaction conditions. This affords an oxidative coupling of methane dataset comprised of 12 708 data po
Epidural pressure was significantly related to posture. These pressure changes correlated with the development of cauda equina symptoms. The increase of epidural pressure by posture may induce compression of the cauda equina. These pressure changes may explain the postural dependency in eliciting symptoms.
The pressure was high in spinal stenosis and low in normal individuals. The increase of epidural pressure at simple walking was higher than walking with lumbar flexion. Intermittent compression to the nerve roots during walking may be a cause of neurogenic intermittent claudication.
Materials informatics has been gaining popularity with the rapid development of computational materials science. However, collaborations between information science and materials science have not yet reached the success. There are several issues which need to be overcome in order to establish the field of materials informatics. Construction of material big data, implementation of machine learning, and platform design for materials discovery are discussed with potential solutions.
Undiscovered perovskite materials for applications in capturing solar lights are explored through the implementation of data science. In particular, 15000 perovskite materials data is analyzed where visualization of the data reveals hidden trends and clustering of data. Random forest classification within machine learning is used in order to predict the band gap of perovskite materials where 18 physical descriptors are revealed to determine the band gap. With trained random forest, 9328 perovski
Abstract Catalysis research is on the verge of experiencing a paradigm shift regarding how catalysts are designed and characterized due to the rise of catalyst informatics. The details of catalyst informatics are reviewed where the following three key concepts are proposed: catalyst data, catalyst data to catalyst design via data science, and catalyst platform. Additionally, progress and opportunities within catalyst informatics are explored and introduced. If the field of catalyst informatics g
All epitaxial oxide magnetic tunnel junctions, ${\mathrm{La}}_{0.7}{\mathrm{Sr}}_{0.3}{\mathrm{MnO}}_{3}/{\mathrm{SrTiO}}_{3}/{\mathrm{SrRuO}}_{3}$ trilayer films, composed of ferromagnetic and metallic electrodes were fabricated on STO(001) substrates. Inverse tunnel magnetoresistance (TMR), i.e., higher and lower junction resistance levels in parallel and anti-parallel magnetization configurations, respectively, was observed, indicating the negative spin polarization of ${\mathrm{SrRuO}}_{3}$
The contact pressure exerted by lumbar disc herniation on the nerve roots was recorded during surgical intervention, and the mean pressure was 53 mm Hg. The magnitude of nerve root pressure was not correlated with the degree of straight leg raising, but with the severity of neurologic deficits.
A significant posture-dependent difference of the dural sac cross-sectional area at the level of intervertebral disc in asymptomatic volunteers has been demonstrated. When the posture changed from supine to standing position, lumbar dural sac volume expanded by the increased pressure of cerebrospinal fluid, and the dural sac cross-sectional area increased. The smallest values were found in the supine position.
Abstract Catalysts for oxidative coupling of methane (OCM) are explored using data science and 1868 OCM catalysts from literature data. Machine learning reveals the descriptors responsible for determining the C 2 yield produced during the OCM reaction. Trained machine predicts 56 undiscovered catalysts with corresponding conditions for OCM reactions achieving a C2 yield over 30 %. First principle calculations are implemented to evaluate the predicted catalysts where the activation of CH 4 , CH 3
Compression of the spinal nerve roots may occur clinically at multiple levels at the same time; however, the basic pathophysiology of multi-level compression is largely unknown. In this study, the intraneural blood flow was analyzed continuously in the uncompressed segment between two compression balloons, with a pig used as an experimental model and a thermal diffusion method. At 10 mm Hg compression, there was a 64% reduction of total blood flow in the uncompressed segment compared with pre-co
Estimation of activation energies within heterogeneous catalytic reactions is performed using machine learning and catalysts dataset. In particular, descriptors for determining activation energy are revealed within the 788 activation energy dataset. With the implementation of machine learning and chosen descriptors, activation energy can be instantly predicted with over 90% accuracy during cross-validation. Thus, rapid estimation of activation energies within heterogeneous catalytic reactions ca
Graphene decorated with Fe clusters is proposed to be a possible alternative catalyst for the hydrogenation and dehydrogenation reactions of MgH<sub>2</sub>.
