General-purpose 3D modeling in chemistry encompasses molecules and materials, requiring both generative and predictive capabilities. However, most existing AI approaches are optimized for a single domain (molecules or materials) and a single task (generation or prediction), which limits representation sharing and transfer. We introduce Zatom-1, a cross-domain, general-purpose model architecture that unifies generative and predictive learning of 3D molecules and materials. Zatom-1 is a deliberately simplified Transformer trained with a multimodal flow matching objective that jointly models discrete atom types and continuous 3D geometries. This approach supports scalable pretraining with predictable gains as model capacity increases, while enabling fast and stable sampling. We use cross-domain generative pretraining as a universal initialization for downstream multi-task prediction of properties, energies, and forces. Empirically, Zatom-1 outperforms or competes with specialized baselines on both multi-task generative and predictive benchmarks in data-controlled settings, while improving generative inference speed by more than an order of magnitude. Our experiments demonstrate positive predictive transfer between data domains from joint generative pretraining: modeling materials during generative pretraining improves molecular property prediction accuracy. Open-source code and model weights are freely available at https://github.com/Zatom-AI/zatom.

翻译：化学领域的通用3D建模涵盖分子与材料两类体系，既需要生成能力也需要预测能力。然而，现有AI方法大多针对单一领域（分子或材料）和单一任务（生成或预测）进行优化，这限制了表示共享与迁移能力。我们提出Zatom-1——一种跨领域通用模型架构，统一了3D分子与材料的生成式学习和预测性学习。Zatom-1采用经过刻意简化的Transformer架构，通过多模态流匹配目标进行训练，该目标联合建模离散原子类型和连续3D几何结构。该方法支持可扩展预训练，随着模型容量提升可预测性地获得性能增益，同时实现快速稳定的采样。我们将跨领域生成式预训练作为下游多任务预测（性质、能量、力）的通用初始化方案。实验表明，在受控数据条件下，Zatom-1在多任务生成与预测基准测试中优于或媲美专业基线模型，同时将生成推理速度提升超过一个数量级。我们的实验证明了联合生成预训练带来的跨数据领域正向预测迁移：在生成预训练阶段对材料进行建模可提升分子性质预测精度。开源代码与模型权重已在https://github.com/Zatom-AI/zatom 开放获取。