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.

通过引入新的扩散式生成过程和适配器模块，本研究提出了MatterGen模型，用于生成各类稳定多样的无机材料，并可以通过微调以满足广泛的性质限制。该模型生成的结构物相较于先前的生成模型是新奇且稳定的，同时与局部能量最小值相比接近15倍。经过微调后，MatterGen成功地生成了具有所需化学、对称性、机械、电子和磁性性质的稳定新材料。本研究还展示了多性质材料设计能力，提出了具有高磁密度和低供应链风险的结构物。我们相信生成材料的质量和MatterGen的广泛能力代表了材料设计通用生成模型的重大进展。

MatterGen: 一种用于无机材料设计的生成模型