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 employment of CliqueFlowmer in specialized materials optimization problems and support interdisciplinary research, we open-source our code at https://github.com/znowu/CliqueFlowmer.

翻译：深度学习的最新进展启发了基于神经网络的计算材料发现方法。该领域中的大量问题涉及寻找能够优化目标特性的材料。然而，由于采用最大似然训练，当前日益流行的生成建模方法难以大胆探索材料空间中具有吸引力的区域。本研究提出了一种基于离线模型优化（MBO）的替代性计算材料发现技术，该技术将目标材料特性的直接优化融合到生成过程中。为此，我们引入了一个领域专用模型CliqueFlowmer，该模型将基于团结构的MBO最新进展与Transformer及流生成机制相结合。我们验证了CliqueFlowmer的优化能力，并证明其生成的材料性能显著优于生成式基线方法所提供的材料。为促进CliqueFlowmer在专业材料优化问题中的应用并支持跨学科研究，我们在https://github.com/znowu/CliqueFlowmer开源了相关代码。