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Proceedings of the AAAI Conference on Artificial Intelligence

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Physical-regularized Hierarchical Generative Model for Me...
Qiyuan Chen, · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Qiyuan Chen University of Wisconsin - Madison
  • Ajay Annamareddy University of Wisconsin - Madison
  • Ying-Fei Li Stanford University
  • Dane Morgan University of Wisconsin - Madison
  • Bu Wang University of Wisconsin - Madison

DOI:

https://doi.org/10.1609/aaai.v40i1.36967

Abstract

Disordered materials such as glasses, unlike crystals, lack long‑range atomic order and have no periodic unit cells, yielding a high‑dimensional configuration space with widely varying properties. The complexity not only increases computational costs for atomistic simulations but also makes it difficult for generative AI models to deliver accurate property predictions and realistic structure generation. In this work, we introduce GlassVAE, a hierarchical graph variational autoencoder that uses graph representations to learn compact, translation‑, and permutation‑invariant embeddings of atomic configurations. The resulting structured latent space not only enables efficient generation of novel, physically plausible structures but also supports exploration of the glass energy landscape. To enforce structural realism and physical fidelity, we augment GlassVAE with two physics‑informed regularizers: a radial distribution function (RDF) loss that captures characteristic short‑ and medium‑range ordering and an energy regression loss that reflects the broad configurational energetics. Both theoretical analysis and experimental results highlight the critical impact of these regularizers. By encoding high‑dimensional atomistic data into a compact latent vector and decoding it into structures with accurate energy predictions, GlassVAE provides a fast, physics‑aware path for modeling and designing disordered materials.

How to Cite

Chen, Q., Annamareddy, A., Li, Y.-F., Morgan, D., & Wang, B. (2026). Physical-regularized Hierarchical Generative Model for Metallic Glass Structural Generation and Energy Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 83–91. https://doi.org/10.1609/aaai.v40i1.36967

Issue

Section

AAAI Technical Track on Application Domains I

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