Ryotaro Okabe

Ryotaro Okabe

he/him

Ph.D. candidate at MIT

Posts by Collection

portfolio

publications

Metal-Organic Framework Photoconductivity via Time-Resolved Terahertz Spectroscopy

Published in Journal of the American Chemical Society, 2019

Investigation of metal-organic framework photoconductivity using time-resolved terahertz spectroscopy techniques.

Recommended citation: Pattengale, B., Neu, J., Ostresh, S., Hu, G., Spies, J.A., Okabe, R., Brudvig, G.W., & Schmuttenmaer, C.A. (2019). Metal-Organic Framework Photoconductivity via Time-Resolved Terahertz Spectroscopy. Journal of the American Chemical Society 141, 9793–9797.
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A Conductive Metal-Organic Framework Photoanode

Published in Chemical Science, 2020

Development and characterization of conductive metal-organic frameworks as photoanodes for solar energy applications.

Recommended citation: Pattengale, B., Freeze, J.G., Guberman-Pfeffer, M., Okabe, R., Ostresh, S., Chaudhuri, S., Batista, V.S., & Schmuttenmaer, C.A. (2020). A Conductive Metal-Organic Framework Photoanode. Chemical Science 11, 9593–9603.
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Machine Learning Magnetism Classifiers from Atomic Coordinates

Published in iScience, 2022

Development of machine learning classifiers for predicting magnetic properties directly from atomic coordinates.

Recommended citation: Merker, H.A., Heiberger, H., Nguyen, L., Liu, T., Chen, Z., Andrejevic, N., Drucker, N.C., Okabe, R., Wang, Y., Smidt, T., & Li, M. (2022). Machine Learning Magnetism Classifiers from Atomic Coordinates. iScience 25, 10, 105192.
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Challenges and Opportunities of Machine Learning on Neutron and X-ray Scattering

Published in Synchrotron Radiation News, 2022

Analysis of challenges and opportunities in applying machine learning to neutron and X-ray scattering data analysis.

Recommended citation: Drucker, N.C., Liu, T., Chen, Z., Okabe, R., Chotrattanapituk, A., Nguyen, T., Wang, Y., & Li, M. (2022). Challenges and Opportunities of Machine Learning on Neutron and X-ray Scattering. Synchrotron Radiation News 35, 16-20.
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Topological superconductors from a materials perspective

Published in Chemistry of Materials, 2023

Materials perspective on topological superconductors, covering synthesis, characterization, and applications.

Recommended citation: Mandal, M., Drucker, N.C., Siriviboon, P., Nguyen, T., Boonkird, A., Lamichhane, T.N., Okabe, R., Chotrattanapituk, A., & Li, M. (2023). Topological superconductors from a materials perspective. Chemistry of Materials 35, 16, 6184–6200.
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Materials Informatics for the Development and Discovery of Future Magnetic Materials

Published in IEEE Magnetic Letters, 2023

Comprehensive overview of materials informatics approaches for developing and discovering next-generation magnetic materials.

Recommended citation: Okabe, R., Li, M., Iwasaki, Y., Regnault, N., Felser, C., Shirai, M., Kovacs, A., Schrefl, T., & Hirohata, A. (2023). Materials Informatics for the Development and Discovery of Future Magnetic Materials. IEEE Magnetic Letters 14, 1-5.
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Incipient Nematicity from Electron Flat Bands in a Kagome Metal

Published in arXiv, 2024

Study of incipient nematicity arising from electron flat bands in kagome metal systems.

Recommended citation: Drucker, N.C., Nguyen, T., Mandal, M., Siriviboon, P., Quan, Y., Boonkird, A., Okabe, R., Li, F., Burrage, K., Funuma, F., Matsuda, M., Abernathy, D., Williams, T., Chi, S., Ye, F., Nelson, C., Liao, B., Volkov, P., & Li, M. (2024). Incipient Nematicity from Electron Flat Bands in a Kagome Metal. arXiv preprint arXiv:2401.17141.
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Tetris-Inspired Detector with Neural Network for Radiation Mapping

Published in Nature Communications, 2024

Innovative Tetris-inspired detector design combined with neural networks for efficient radiation mapping applications.

Recommended citation: Okabe, R., Xue, S., Vavrek, J., Yu, J., Pavlovsky, R., Negut, V., Quiter, B., Cates, J., Liu, T., Forget, B., Jegelka, S., Kohse, G., Hu, L-W., & Li, M. (2024). Tetris-Inspired Detector with Neural Network for Radiation Mapping. Nature Communications 15, 3061.
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Precise Fermi-level engineering in a topological Weyl semimetal via fast ion implantation

Published in Applied Physics Reviews, 2024

Precise control of Fermi level in topological Weyl semimetals through fast ion implantation techniques.

Recommended citation: Mandal, M., Chotrattanapituk, A., Woller, K., Xu, H., Mao, N., Okabe, R., Boonkird, A., Nguyen, T., Drucker, N.C., Momiki, T., Li, J., Kong, J., & Li, M. (2024). Precise Fermi-level engineering in a topological Weyl semimetal via fast ion implantation. Applied Physics Reviews 11, 021429.
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Virtual Node Graph Neural Network for Full Phonon Prediction

Published in Nature Computational Science, 2024

Innovative virtual node graph neural network architecture for comprehensive phonon property prediction from atomic coordinates.

Recommended citation: Okabe, R., Chotrattanapituk, A., Boonkird, A., Andrejevic, N., Fu, X., Jaakkola, T.S., Song, Q., Nguyen, T., Drucker, N.C., Mu, S., Liao, B., Cheng, Y., & Li, M. (2024). Virtual Node Graph Neural Network for Full Phonon Prediction. Nature Computational Science 4, 522-531.
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Design of efficient solvent-suppression scheme in solid-state NMR: Echo-formation mechanism and dephasing by adiabatic inversion pulses

Published in Journal of Magnetic Resonance, 2024

Development of efficient solvent suppression techniques for solid-state NMR with theoretical analysis of echo-formation mechanisms.

Recommended citation: Matsunaga, T., Okabe, R., & Ishii, Y. (2024). Design of efficient solvent-suppression scheme in solid-state NMR: Echo-formation mechanism and dephasing by adiabatic inversion pulses. Journal of Magnetic Resonance (submitted).

Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures

Published in Advanced Materials, 2024

Development of ensemble-embedding graph neural networks for direct prediction of optical spectra from crystal structures.

Recommended citation: Hung, N.T., Okabe, R., Chotrattanapituk, A., & Li, M. (2024). Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structure. Advanced Materials, 2409175.
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Large Language Model-Guided Prediction Toward Quantum Materials Synthesis

Published in arXiv, 2024

Novel application of large language models for predicting quantum material synthesis pathways and conditions.

Recommended citation: Okabe, R., West, Z., Chotrattanapituk, A., Cheng, M., Cordova Carrizales, D., Xie, W., Cava, R.J., & Li, M. (2024). Large Language Model-Guided Prediction Toward Quantum Materials Synthesis. arXiv preprint arXiv:2410.20976.
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Quantum Theory of X-ray Photon Correlation Spectroscopy

Published in arXiv, 2024

Theoretical framework for X-ray photon correlation spectroscopy with quantum mechanical foundations.

Recommended citation: Siriviboon, P., Fu, C., Landry, M., Okabe, R., Cordova Carrizales, D., Wang, Y., & Li, M. (2024). Quantum Theory of X-ray Photon Correlation Spectroscopy. arXiv preprint arXiv:2412.03635.
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AI-driven materials design: a mini-review

Published in Nature Materials, 2025

Comprehensive review of AI-driven approaches for materials design, covering recent advances and future directions in computational materials science.

Recommended citation: Cheng, M., Fu, C., Okabe, R., Chotrattanapituk, A., Boonkird, A., Hung, N.T., & Li, M. (2025). AI-driven materials design: a mini-review. Nature Materials.
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AI-driven defect engineering in advanced thermoelectric materials

Published in Advanced Materials, 2025

Application of AI-driven approaches for defect engineering in thermoelectric materials to enhance their performance.

Recommended citation: Fu, C., Cheng, M., Hung, N.T., Rha, E., Chen, Z., Okabe, R., Cordova Carrizales, D., Mandal, M., Cheng, Y.Q., & Li, M. (2025). AI-driven defect engineering in advanced thermoelectric materials. Advanced Materials 37, 2505642.
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Closing the superconducting gap with AI

Published in Newton, 2025

Machine learning approach to predict and close superconducting gaps in quantum materials.

Recommended citation: Cheng, M., Okabe, R., & Li, M. (2025). Closing the superconducting gap with AI. Newton 1, 100093.
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Tuning Chiral Anomaly Signature in a Dirac Semimetal via Fast-Ion Implantation

Published in arXiv, 2025

Experimental study demonstrating control of chiral anomaly signatures in Dirac semimetals through fast-ion implantation techniques.

Recommended citation: Mandal, M., Rha, E., Chotrattanapituk, A., Cordova Carrizales, D., Lygo, A., Woller, K.B., Cheng, M., Okabe, R., Zhu, G., Mak, K., Fu, C.L., Liu, C., Wu, L., Zhu, Y., Stemmer, S., & Li, M. (2025). Tuning Chiral Anomaly Signature in a Dirac Semimetal via Fast-Ion Implantation. arXiv preprint arXiv:2507.17972.
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Structural Constraint Integration in a Generative Model for the Discovery of Quantum Materials

Published in Nature Materials, 2025

We present a novel approach to integrating structural constraints in generative models for quantum material discovery, enabling more accurate predictions of material properties through advanced machine learning techniques.

Recommended citation: Okabe, R., Cheng, M., Chotrattanapituk, A., Mandal, M., Mak, K., Cordova Carrizales, D., Hung, N.T., Fu, X., Han, B., Wang, Y., Xie, W., Cava, R.J., Jaakkola, T.S., Cheng, Y., Li, M. (2024). Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates. Nature Materials.
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