APS Logo

AI for Materials Discovery II

ORAL · MAR-S37 · ID: 3091519

Presentations

  • Unveiling intermediate metallicity in epitaxial VO2 using nano-spectroscopy aided by machine learning

    ORAL

    –

    Presenters

    • Alyssa Bragg

      University of Minnesota

    Authors

    • Alyssa Bragg

      University of Minnesota

    • Elihu Anouchi

      Bar Ilan University

    • Liam Thompson

      University of Minnesota

    • William Cho

      University of Minnesota

    • Nitzan Yehudit Hirshberg

      University of Minnesota

    • Brayden Lukaskawcez

      University of Minnesota

    • Devon Uram

      University of Minnesota, Harvard University

    • Madison Garber

      University of Minnesota

    • Hayden Binger

      University of Minnesota

    • Amos Sharoni

      Bar Ilan University

    • Alexander S McLeod

      University of Minnesota

    View abstract →

  • A Universal Deep Learning Framework for Materials X-ray Absorption Spectra

    ORAL

    –

    Publication: arXiv:2409.19552

    Presenters

    • Deyu Lu

      Brookhaven National Laboratory (BNL)

    Authors

    • Deyu Lu

      Brookhaven National Laboratory (BNL)

    • Shubha Kharel

      Brookhaven National Laboratory

    • Xiaohui Qu

      Brookhaven National Laboratory (BNL)

    • Fanchen Meng

      Brookhaven National Laboratory (BNL)

    • Matthew R Carbone

      Brookhaven National Lab

    View abstract →

  • Machine Learning Analysis of high-dimensional ARPES Data for Nd<sub>1-x</sub>Sr<sub>x</sub>NiO<sub>3</sub>

    ORAL

    –

    Presenters

    • Yu Zhang

      University of Florida

    Authors

    • Yu Zhang

      University of Florida

    • Yong Zhong

      Stanford University

    • Nhat Huy Mai Tran

      University of Florida

    • Shuyi Li

      University of Florida

    • Kyuho Lee

      Stanford University, Massachusetts Institute of Technology

    • Harold Y Hwang

      Stanford University

    • Zhi-Xun Shen

      Stanford University

    • Chunjing Jia

      University of Florida

    View abstract →

  • Designing Materials for Catalysis via Systematic Experiments and Artificial Intelligence

    ORAL

    –

    Publication: G. Bellini et al., Angew. Chem. Int. Ed., DOI: 10.1002/anie.202417812

    Presenters

    • Lucas Foppa

      Fritz Haber Institute of the Max Planck Society, The NOMAD Laboratory at FHI, Max Planck Society

    Authors

    • Lucas Foppa

      Fritz Haber Institute of the Max Planck Society, The NOMAD Laboratory at FHI, Max Planck Society

    • Matthias Scheffler

      The NOMAD Laboratory at FHI, Max Planck Society

    View abstract →

  • Material Mapping Using Experimental Data and Crystal Graph Neural Networks

    ORAL

    –

    Publication: Jia, X., Aziz, A., Hashimoto, Y. et al. Dealing with the big data challenges in AI for thermoelectric materials. Sci. China Mater. 67, 1173–1182 (2024).

    Presenters

    • Yusuke Hashimoto

      FRIS, Tohoku University

    Authors

    • Yusuke Hashimoto

      FRIS, Tohoku University

    • Xue Jia

      AIMR, Tohoku University

    • Hao Li

      AIMR, Tohoku University

    • Takaaki Tomai

      FRIS, Tohoku University

    View abstract →

  • Real-time Autonomous Optimization of Thin Film Growth

    ORAL

    –

    Publication: 1] Xu. X, Wang. W. Multiferroic hexagonal ferrites (h-RFeO3, R = Y, Dy-Lu): a brief experimental review.<br>Mod. Phys. Lett. B. 28 (21) (2014).<br>[2] H. Yokota, T. Nozue, S. Nakamura, M. Fukunaga, and A. Fuwa, Examination of Ferroelectric and<br>Magnetic Properties of Hexagonal ErFeO3 Thin Films, Jpn. J. Appl. Phys. 54, 10NA10 (2015).<br>[3] K. K. Sinha, Growth and Characterization of Hexagonal Rare-Earth Ferrites (h-RFeO3; R = Sc, Lu, Yb),<br>The University of Nebraska - Lincoln PP - United States -- Nebraska, 2018.<br>[4] J. Kasahara, T. Katayama, S. Mo, A. Chikamatsu, Y. Hamasaki, S. Yasui, M. Itoh, and T. Hasegawa,<br>Room-Temperature Antiferroelectricity in Multiferroic Hexagonal Rare-Earth Ferrites, ACS Appl. Mater.<br>Interfaces 13, 4230 (2021).<br>[5] J. M. Costantini, T. Ogawa, A. S. I. Bhuian, and K. Yasuda, Cathodoluminescence Induced in Oxides by<br>High-Energy Electrons: Effects of Beam Flux, Electron Energy, and Temperature, J. Lumin. 208, 108<br>(2019).<br>[6] Liang. H. et al. Application of machine learning to reflection high-energy electron diffraction images<br>for automated structural phase mapping. Phys. Rev. Materials. 6, 063805 (2022).<br>[7] Wang. A. et al. Benchmarking active learning strategies for materials optimization and discovery.<br>Oxford Open Materials Science, 2 (1) (2022).<br>[8] Kusne. A. G. et al. On-the-fly closed-loop materials discovery via Bayesian active learning. Nat.<br>Commun. 2020 111 11, 1–11 (2020).

    Presenters

    • Haotong Liang

      University of Maryland College Park

    Authors

    • Haotong Liang

      University of Maryland College Park

    • Ryan S Paxson

      University of Maryland, University of Maryland, College Park

    • Yunlong Sun

      The University of Tokyo

    • Aaron Kusne

      University of Maryland College Park

    • Mikk Lippmaa

      The University of Tokyo

    • Ichiro Takeuchi

      University of Maryland College Park, University of Maryland, University of Maryland, College Park

    View abstract →

  • Machine learning guided study of BCS superconductors

    ORAL

    –

    Presenters

    • Trevor David Rhone

      Rensselaer Polytechnic Institute

    Authors

    • Trevor David Rhone

      Rensselaer Polytechnic Institute

    • Dylan Sheils

      Rensselaer Polytechnic Institute

    • Yoshiharu Krockenberger

      NTT Basic Research Labs

    View abstract →