Molecular dynamics (MD) simulations underpin modern computational drug discovery, materials science, and biochemistry. Recent machine learning models provide high-fidelity MD predictions without the need to repeatedly solve quantum mechanical forces, enabling significant speedups over conventional pipelines. Yet many such methods typically enforce strict equivariance and rely on sequential rollouts, thus limiting their flexibility and simulation efficiency. They are also commonly single-task, trained on individual molecules and fixed timeframes, which restricts generalization to unseen compounds and extended timesteps. To address these issues, we propose Atomistic Transformer Operator for Molecules (ATOM), a pretrained transformer neural operator for multitask molecular dynamics. ATOM adopts a quasi-equivariant design that requires no explicit molecular graph and employs a temporal attention mechanism, allowing for the accurate parallel decoding of multiple future states. To support operator pretraining across chemicals and timescales, we curate TG80, a large, diverse, and numerically stable MD dataset with over 2.5 million femtoseconds of trajectories across 80 compounds. ATOM achieves state-of-the-art performance on established single-task benchmarks, such as MD17, RMD17 and MD22. After multitask pretraining on TG80, ATOM shows exceptional zero-shot generalization to unseen molecules across varying time horizons. We believe ATOM represents a significant step toward accurate, efficient, and transferable molecular dynamics models.

翻译：分子动力学（MD）模拟是现代计算药物发现、材料科学和生物化学的基础。近期机器学习模型能够提供高保真度的MD预测，无需重复求解量子力学力，从而实现相比传统流程显著的加速。然而，这类方法通常强制严格等变性并依赖序列滚动求解，限制了其灵活性和模拟效率。此外，它们通常为单任务模型，仅在单个分子和固定时间步长上训练，导致难以泛化至未见化合物和延长的时间步长。为解决这些问题，我们提出分子原子性Transformer算子（ATOM），一种用于多任务分子动力学的预训练Transformer神经算子。ATOM采用准等变设计，无需显式分子图，并结合时间注意力机制，能够并行精确解码多个未来状态。为支持跨化学物质和时间尺度的算子预训练，我们构建了TG80数据集——一个包含80种化合物、超过250万飞秒轨迹的大规模、多样且数值稳定的MD数据集。在MD17、RMD17和MD22等已建立的单任务基准上，ATOM达到最先进性能。经过TG80多任务预训练后，ATOM在跨不同时间尺度的未见分子上展现出卓越的零样本泛化能力。我们相信ATOM标志着朝着精确、高效且可迁移的分子动力学模型迈出了重要一步。