The design of functional materials with desired properties is essential in driving technological advances in areas like energy storage, catalysis, and carbon capture. Generative models provide a new paradigm for materials design by directly generating entirely novel materials given desired property constraints. Despite recent progress, current generative models have low success rate in proposing stable crystals, or can only satisfy a very limited set of property constraints. Here, we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. To enable this, we introduce a new diffusion-based generative process that produces crystalline structures by gradually refining atom types, coordinates, and the periodic lattice. We further introduce adapter modules to enable fine-tuning towards any given property constraints with a labeled dataset. Compared to prior generative models, structures produced by MatterGen are more than twice as likely to be novel and stable, and more than 15 times closer to the local energy minimum. After fine-tuning, MatterGen successfully generates stable, novel materials with desired chemistry, symmetry, as well as mechanical, electronic and magnetic properties. Finally, we demonstrate multi-property materials design capabilities by proposing structures that have both high magnetic density and a chemical composition with low supply-chain risk. We believe that the quality of generated materials and the breadth of MatterGen's capabilities represent a major advancement towards creating a universal generative model for materials design.

翻译：功能材料的设计——即按所需性能创造新材料——是推动储能、催化、碳捕获等领域技术进步的核心。生成模型通过直接生成满足特定性能约束的全新材料，为材料设计提供了全新范式。尽管近期取得进展，当前生成模型在提出稳定晶体结构方面成功率较低，或仅能满足极为有限的性能约束条件。本文提出MatterGen——一种能够生成周期表中稳定、多样无机材料，并可微调以引导生成过程满足广泛性能约束的模型。为实现这一目标，我们引入了基于扩散的新型生成过程，通过逐步优化原子类型、坐标和周期性晶格来产生晶体结构。进一步地，我们设计了适配器模块，使其能够利用标注数据集针对任意给定性能约束进行微调。与先前生成模型相比，MatterGen生成的结构具有新颖性和稳定性的概率提升两倍以上，且更接近局部能量最小值的程度超过15倍。微调后，MatterGen成功生成了满足所需化学性质、对称性以及机械、电子和磁学性能的稳定新型材料。最后，我们通过提出兼具高磁密度与低供应链风险化学成分的结构，展示了多性能材料设计能力。我们相信，MatterGen在生成材料质量与功能广度上的突破，标志着向创建通用材料设计生成模型迈出了关键一步。