AI-driven Generative and Inverse Materials Design and Discovery

Presentations

• Accurate first principles data and their role in machine learning material models

  Invited-In-person · Invited

  March 16, 2026, 12:00 PM – March 16, 2026, 12:36 PM

  Publication: Weiluo Ren, Weizhong Fu, Xiaojie Wu, Ji Chen, Towards the ground state of molecules via diffusion Monte Carlo on neural networks, Nat Commun 14, 1860 (2023).
  Du Jiang et al., Neural Scaling Laws Surpass Chemical Accuracy for the Many-Electron Schrödinger Equation, arXiv 2508.02570 (2025).
  Weizhong Fu et al., Local Pseudopotential Unlocks the True Potential of Neural Network-based Quantum Monte Carlo, arXiv 2505.19909 (2025).
  Mouyang Cheng et al., Predicting Macroscopic Properties of Amorphous Monolayer Carbon via Pair Correlation Function, Chinese Phys. Lett. 42, 066101 (2025).

  Presenters

  • Ji Chen

    • Peking Univ

  Authors

  • Ji Chen

    • Peking Univ

  View abstract →

• Learning to predict superconductivity

  Invited-In-person · Invited

  March 16, 2026, 12:36 PM – March 16, 2026, 1:12 PM

  Publication: arXiv:2510.07373

  Presenters

  • Omri Lesser

    • Cornell University

  Authors

  • Omri Lesser

    • Cornell University

  • YANJUN LIU

    • Cornell University

  • Natalie Maus

  • Aaditya Panigrahi

    • Cornell University

  • Krishnanand Mallayya

    • Cornell University

  • Leslie Schoop

    • Princeton University

  • Jacob Gardner

  • Eun-Ah Kim

    • Cornell University

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• Physics-Guided Machine Learning for Predicting Lattice Parameters of Crystals

  Oral-In-person

  March 16, 2026, 1:12 PM – March 16, 2026, 1:24 PM

  Publication: [1] Mohammad Alghadeer, et al. "Machine Learning Prediction of Materials Properties from Chemical Composition: Status and Prospects." Chemical Physics Reviews, 5(4), 041313 (2024).
  
  [2] Yousef A. Alghofaili, et al. "Accelerating Materials Discovery through Machine Learning: Predicting Crystallographic Symmetry Groups." Journal of Physical Chemistry C, 127(33), 16645–16653 (2023).
  
  [3] Abdulmohsen Alsaui, et al. "Highly Accurate Machine Learning Prediction of Crystal Point Groups for Ternary Materials from Chemical Formula." Scientific Reports, 12(1) (2022).

  Presenters

  • Nufida Aisyah

    • King Fahd University of Petroleum and Minerals

  Authors

  • Nufida Aisyah

    • King Fahd University of Petroleum and Minerals

  • Fahhad Alharbi

    • King Fahd University of Petroleum and Minerals

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• VNFlow: Integrating Normalizing Flows with Variational Autoencoders for Molecular Design

  Oral-In-person

  March 16, 2026, 1:24 PM – March 16, 2026, 1:36 PM

  Publication: https://doi.org/10.1186/s13321-025-01104-2

  Presenters

  • Jiri Hostas

    • National Research Council Canada

  Authors

  • Jiri Hostas

    • National Research Council Canada

  • Hang Hu

    • National Research Council Canada

  • Mohammad Sajjad Ghaemi

  • Junan Lin

    • National Research Council Canada

  • Anguang Hu

    • Suffield Research Centre, DRDC

  • Hsu Kiang (James) Ooi

    • National Research Council Canada

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• AI-Enabled Discovery and Characterization of Topological Superconductors

  Oral-In-person

  March 16, 2026, 1:36 PM – March 16, 2026, 1:48 PM

  Presenters

  • Tzu-Chi Hsieh

    • University of Notre Dame

  Authors

  • Tzu-Chi Hsieh

    • University of Notre Dame

  • Sheng-Jie Huang

    • Max Planck Institute for the Physics of Complex Systems

  • Yi-Ting Hsu

    • University of Notre Dame

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• BiRLNN: Bidirectional Reinforcement-Learning Neural Network for Constrained Molecular Design

  Oral-In-person

  March 16, 2026, 1:48 PM – March 16, 2026, 2:00 PM

  Publication: https://doi.org/10.21203/rs.3.rs-7540516/v1

  Presenters

  • Junan Lin

    • National Research Council Canada

  Authors

  • Junan Lin

    • National Research Council Canada

  • Jiri Hostas

    • National Research Council Canada

  • Anguang Hu

    • Suffield Research Centre, DRDC

  • Hang Hu

    • National Research Council Canada

  • Hsu Kiang (James) Ooi

    • National Research Council Canada

  • Mohammad Sajjad Ghaemi

  View abstract →

• Machine-Assisted Design of Patchy Polygons for Mesoscale Assembly of Superlattices

  Oral-In-person

  March 16, 2026, 2:00 PM – March 16, 2026, 2:12 PM

  Presenters

  • Po-An Lin

    • Duke University

  Authors

  • Po-An Lin

    • Duke University

  • Krystal Wang

  • Sophia Sang

  • Gaurav Arya

    • Duke University

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• Inorganic Crystals Graphlet Bank: Atomic Cluster-Based Representation of Inorganic Crystals

  Oral-In-person

  March 16, 2026, 2:12 PM – March 16, 2026, 2:24 PM

  Publication: Lesser et al., arXiv:2510.07373 (2025).

  Presenters

  • Aaditya Panigrahi

    • Cornell University

  Authors

  • Aaditya Panigrahi

    • Cornell University

  • YANJUN LIU

    • Cornell University

  • Omri Lesser

    • Cornell University

  • Eun-Ah Kim

    • Cornell University

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• Data-Driven Discovery of Superconductivity within the Inorganic Crystals Graphlet Bank

  Oral-In-person

  March 16, 2026, 2:24 PM – March 16, 2026, 2:36 PM

  Presenters

  • YANJUN LIU

    • Cornell University

  Authors

  • YANJUN LIU

    • Cornell University

  • Aaditya Panigrahi

    • Cornell University

  • Omri Lesser

    • Cornell University

  • Eun-Ah Kim

    • Cornell University

  View abstract →

• Data-Efficient AI Framework for Polymer Design through Hierarchical Representation

  Oral-In-person

  March 16, 2026, 2:36 PM – March 16, 2026, 2:48 PM

  Publication: arXiv:2502.00910

  Presenters

  • Ge Sun

    • New York University

  Authors

  • Ge Sun

    • New York University

  • Ming Han

    • University of Chicago

  • Yuan Tian

    • New York University

  • Gervasio Zaldivar

    • University of Chicago

  • Juan de Pablo

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• Computational framework for efficient navigation of atomic configurational space using physically guided actions and transformations

  Oral-In-person

  March 16, 2026, 2:48 PM – March 16, 2026, 3:00 PM

  Presenters

  • Jason Rivas

    • University of Colorado, Boulder

  Authors

  • Jason Rivas

    • University of Colorado, Boulder

  • Artem Pimachev

  • Sanghamitra Neogi

    • University of Colorado, Boulder

  View abstract →