{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:25:06Z","timestamp":1773800706410,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Disordered materials such as glasses, unlike crystals, lack long\u2011range atomic order and have no periodic unit cells, yielding a high\u2011dimensional 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\u2011, and permutation\u2011invariant 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\u2011informed regularizers: a  radial distribution function (RDF) loss that captures characteristic short\u2011 and medium\u2011range 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\u2011dimensional atomistic data into a compact latent vector and decoding it into structures with accurate energy predictions, GlassVAE provides a fast, physics\u2011aware path for modeling and designing disordered materials.<\/jats:p>","DOI":"10.1609\/aaai.v40i1.36967","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T22:38:41Z","timestamp":1773787121000},"page":"83-91","source":"Crossref","is-referenced-by-count":0,"title":["Physical-regularized Hierarchical Generative Model for Metallic Glass Structural Generation and Energy Prediction"],"prefix":"10.1609","volume":"40","author":[{"given":"Qiyuan","family":"Chen","sequence":"first","affiliation":[]},{"given":"Ajay","family":"Annamareddy","sequence":"additional","affiliation":[]},{"given":"Ying-Fei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Dane","family":"Morgan","sequence":"additional","affiliation":[]},{"given":"Bu","family":"Wang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/36967\/40929","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/36967\/40929","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T22:38:41Z","timestamp":1773787121000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/36967"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i1.36967","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}