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 information at https://github.com/znowu/CliqueFlowmer.

翻译：近年来，深度学习的发展促进了基于神经网络的计算材料发现（CMD）方法。该领域中的大量问题涉及寻找能够优化目标属性的材料。然而，由于最大似然训练的限制，日益流行的生成建模方法难以大胆探索材料空间中的有吸引力的区域。在本工作中，我们提出了一种基于离线模型优化的替代CMD技术，该技术将目标材料属性的直接优化融合到生成过程中。为此，我们引入了一种领域特定模型，称为CliqueFlowmer，它将基于团的下游模型优化的最新进展整合到Transformer和流程生成中。我们验证了CliqueFlowmer的优化能力，并表明其产生的材料在性能上明显优于生成基线方法。为了支持其在专业材料发现问题中的使用并促进跨学科研究，我们在https://github.com/znowu/CliqueFlowmer上开源了我们的代码并提供了额外项目信息。