用新模型一键生成性能更优的材料，突破传统生成方法局限
Offline Materials Optimization with CliqueFlowmer
- 将图结构优化融入Transformer与生成流，实现材料属性直接优化
- 在多个数据集上生成材料性能显著优于生成基线模型
- 适合材料设计、催化剂开发等需要高效探索新材料的研究者
深度学习推动了计算材料发现（CMD）中基于神经网络的方法发展。许多问题需寻找能优化特定性能的材料，但当前流行的生成建模方法因最大似然训练限制，难以大胆探索材料空间中的高潜力区域。本文提出一种基于离线模型的优化（MBO）的新技术，将目标材料属性的直接优化融合进生成过程。为此，我们引入领域专用模型CliqueFlowmer，结合图结构优化最新进展与Transformer及生成流架构。实验验证该模型具备强大优化能力，所生成材料性能显著优于生成基线。为支持专业材料发现与跨学科研究，我们开源代码、模型权重及相关资源：https://github.com/znowu/CliqueFlowmer, https://colab.research.google.com/drive/1usUg7zezFkcYHlm2MdYwZUNJXf_YkWnY?usp=sharing, https://x.com/kuba_AI/status/2033382617442345321。
原文摘要 · Abstract (English)
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 this model's optimization abilities and show that materials it produces strongly outperform those from generative baselines. To support specialized materials discovery applications and broader interdisciplinary research, we release our code, model weights, and additional project resources at https://github.com/znowu/CliqueFlowmer, https://colab.research.google.com/drive/1usUg7zezFkcYHlm2MdYwZUNJXf_YkWnY?usp=sharing, and https://x.com/kuba_AI/status/2033382617442345321.
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