Offline Materials Optimization with CliqueFlowmer
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-b...
| Authors | Jakub Grudzien Kuba, Benjamin Kurt Miller, Sergey Levine et al. |
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| Primary category | cs.AI |
| Published | 6 March 2026 |
| Last updated at source | 19 April 2026 |
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