[2603.06082] Offline Materials Optimization with CliqueFlowmer

Computer Science > Artificial Intelligence arXiv:2603.06082 (cs) [Submitted on 6 Mar 2026 (v1), last revised 29 Mar 2026 (this version, v3)] Title:Offline Materials Optimization with CliqueFlowmer Authors:Jakub Grudzien Kuba, Benjamin Kurt Miller, Sergey Levine, Pieter Abbeel View a PDF of the paper titled Offline Materials Optimization with CliqueFlowmer, by Jakub Grudzien Kuba and 3 other authors View PDF HTML (experimental) Abstract:Recent advances in deep learning inspired neural network-based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials that optimize a target property. Nevertheless, the increasingly popular generative modeling methods are ineffective at boldly exploring attractive regions of the materials space due to their maximum likelihood training. In this work, we offer an alternative CMD technique based on offline model-based optimization (MBO) that fuses direct optimization of a target material property into generation. To that end, we introduce a domain-specific model, dubbed CliqueFlowmer, that incorporates recent advances of clique-based MBO into transformer and flow generation. We validate CliqueFlowmer's optimization abilities and show that materials it produces strongly outperform those provided by generative baselines. To enable its use in specialized materials discovery problems and support interdisciplinary research, we open-source our code and provide additional project informati...