Shohei Shimizu 研究室

主宰者：Shohei Shimizu

理化学研究所・RIKEN Center for Advanced Intelligence Project

AI 要約（直近 5 年の研究成果）

本研究室は、分子設計を通じた機能性材料の開発に取り組んでいます。特に注目しているのは、光や機械的刺激に応答して性質が変わる分子の設計です。例えば、複数の芳香族分子を含む構造を工夫することで、外力が加わると蛍光の色が黄色から青緑色に変わる材料を開発しています。このような刺激応答性分子は、ポリウレタン樹脂などの高分子に組み込むことで、材料にかかる力や応力を光で「見える化」する仕組みとして機能します。また、有機分子の光吸収特性を化学反応によって制御し、太陽光エネルギーを熱に変換する効率的な材料開発も進めています。分子の構造と光物理特性の関係を理解することで、より広い波長の光を吸収・利用できる物質を実現しようとしています。さらに、有機半導体高分子を合成・設計し、薄膜トランジスタや太陽電池などへの応用を目指した研究も展開しています。これらの材料科学的な研究に加えて、機械学習や統計的因果推論の手法を用いて、複雑なデータから意味のある関係性を抽出する研究も行われています。生細胞の応答予測、医療データの解析、工業システムの異常診断など、データ駆動型の問題解決にも取り組んでおり、理論と実験を組み合わせた多角的なアプローチが特徴です。

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

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研究成果（36 件）

[2025] Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis
DOI: https://doi.org/10.1109/ijcnn64981.2025.11228702
[2025] Outside Back Cover: Hinge‐Like Mechanochromic Mechanophores Based on [2.2]Paracyclophane (Angew. Chem. 37/2025)
DOI: https://doi.org/10.1002/ange.202517347
[2025] Enhanced Photothermal Property of Dithienoindacenodithiophene Molecules by [2 + 2] Cycloaddition–Retroelectrocyclization Reaction for Efficient Solar Steam Generation
DOI: https://doi.org/10.1002/solr.202400803
[2025] Causal models and prediction in cell line perturbation experiments
DOI: https://doi.org/10.1186/s12859-024-06027-7
[2025] Vinylene-bridged naphthalenediimide-based dual-acceptor copolymers for thin-film transistors and solar steam generation
DOI: https://doi.org/10.1039/d5lp00136f
[2025] Outside Back Cover: Hinge‐Like Mechanochromic Mechanophores Based on [2.2]Paracyclophane (Angew. Chem. Int. Ed. 37/2025)
DOI: https://doi.org/10.1002/anie.202517347
[2025] Hinge‐Like Mechanochromic Mechanophore Featuring 2,7‐Bis(phenylethynyl)pyrene
DOI: https://doi.org/10.1002/macp.202500304
[2025] Hinge‐Like Mechanochromic Mechanophores Based on [2.2]Paracyclophane
DOI: https://doi.org/10.1002/anie.202510114
[2025] Hinge‐Like Mechanochromic Mechanophores Based on [2.2]Paracyclophane
DOI: https://doi.org/10.1002/ange.202510114
[2024] Ring-size dependent ratiometric photoluminescence of cyclophane mechanophores
DOI: https://doi.org/10.1039/d4tc00244j

続きを表示（残り 26 件）

[2024] Multi-Domain and Multi-View Oriented Deep Neural Network for Sentiment Analysis in Large Language Models
DOI: https://doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics62450.2024.00045
[2024] Use of prior knowledge to discover causal additive models with unobserved variables and its application to time series data
DOI: https://doi.org/10.1007/s41237-024-00238-1
[2024] Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating
DOI: https://doi.org/10.1109/ijcnn60899.2024.10650130
[2024] #1058 Causal discovery for the volume of white matter in patients undergoing haemodialysis
DOI: https://doi.org/10.1093/ndt/gfae069.846
[2024] Cyclophane-based Supramolecular Mechanophores that Detect and Visualize Small Force
DOI: https://doi.org/10.11618/adhesion.60.233
[2023] Force-Induced Shuttling of Rotaxanes Controls Fluorescence Resonance Energy Transfer in Polymer Hydrogels
DOI: https://doi.org/10.1021/acsami.2c20904
[2023] Special issue: recent developments in causal inference and machine learning vol.2
DOI: https://doi.org/10.1007/s41237-023-00221-2
[2023] Novel MITM attack scheme based on built-in negotiation for blockchain-based digital twins
DOI: https://doi.org/10.1016/j.dcan.2023.11.011
[2023] Information Theoretic Learning-Enhanced Dual-Generative Adversarial Networks With Causal Representation for Robust OOD Generalization
DOI: https://doi.org/10.1109/tnnls.2023.3330864
[2023] BiLSTM and VAE Enhanced Multi-Task Neural Network for Trust-Aware E-Commerce Product Analysis
DOI: https://doi.org/10.1109/trustcom60117.2023.00114
[2023] Digital Twin Enhanced Federated Reinforcement Learning With Lightweight Knowledge Distillation in Mobile Networks
DOI: https://doi.org/10.1109/jsac.2023.3310046
[2023] Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications
DOI: https://doi.org/10.1109/tcss.2023.3259431
[2023] Pneumothorax with Eosinophilia is an Important Diagnostic Clue for Distinguishing Paragonimiasis from Chronic Eosinophilic Pneumonia: A Case Report
DOI: https://doi.org/10.2147/idr.s402392
[2023] Mechanochromic luminescence of phase-separated hydrogels that contain cyclophane mechanophores
DOI: https://doi.org/10.1039/d3qm00579h
[2022] Special issue: Recent developments in causal inference and machine learning
DOI: https://doi.org/10.1007/s41237-022-00173-z
[2022] CNN-GRU Based Deep Learning Model for Demand Forecast in Retail Industry
DOI: https://doi.org/10.1109/ijcnn55064.2022.9892599
[2022] Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks
DOI: https://doi.org/10.3390/ijerph19031228
[2021] Crystal structure of a 1,6-bis(phenylethynyl)pyrene-based cyclophane that exhibits mechanochromic luminescence
DOI: https://doi.org/10.1039/d1me00131k
[2021] Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System
DOI: https://doi.org/10.1109/jiot.2021.3130434
[2021] Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders
DOI: https://doi.org/10.1007/s41060-021-00282-0
[2021] Nonlinear Causal Discovery for High-Dimensional Deterministic Data
DOI: https://doi.org/10.1109/tnnls.2021.3106111
[2021] Estimating individual-level optimal causal interventions combining causal models and machine learning models
[2021] Causal Discovery with Multi-Domain LiNGAM for Latent Factors
DOI: https://doi.org/10.24963/ijcai.2021/289
[2021] Causal Additive Models with Unobserved Variables
[2021] Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber-Physical Systems
DOI: https://doi.org/10.1109/tii.2021.3061419
[2021] DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
DOI: https://doi.org/10.17615/wr4c-0434

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AI 要約（直近 5 年の研究成果）

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関連研究室(8 件)

研究成果（36 件）

科研費（0 件）

所属学会・役職（0 件）