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：功能材料的设计对推动能源存储、催化、碳捕获等领域的技术进步至关重要。生成模型通过直接生成满足特定性能约束的全新材料，为材料设计提供了新范式。尽管近期取得进展，现有生成模型在提出稳定晶体方面的成功率仍然较低，或仅能满足极为有限的性能约束。在此，我们提出MatterGen——该模型能生成跨越元素周期表的稳定、多样的无机材料，并可通过微调将生成过程导向广泛的性能约束。为实现这一目标，我们引入了一种基于扩散的生成过程，通过逐步优化原子类型、坐标和周期晶格来生成晶体结构。我们还引入了适配器模块，使其能利用标注数据集针对任意给定性能约束进行微调。与先前的生成模型相比，MatterGen生成的结构在新颖性和稳定性方面的概率高出两倍以上，且与局部能量最小值的距离缩小了15倍以上。微调后，MatterGen成功生成了具有所需化学组成、对称性以及力学、电子和磁学性能的稳定新颖材料。最后，我们通过提出同时具备高磁密度和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所生成材料的质量及其能力的广度，标志着向创建通用的材料设计生成模型迈出了重要一步。