The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science including drug discovery, energy storage, and semiconductor manufacturing. To address this need, Meta FAIR presents a family of Universal Models for Atoms (UMA), designed to push the frontier of speed, accuracy, and generalization. UMA models are trained on half a billion unique 3D atomic structures (the largest training runs to date) by compiling data across multiple chemical domains, e.g. molecules, materials, and catalysts. We develop empirical scaling laws to help understand how to increase model capacity alongside dataset size to achieve the best accuracy. The UMA small and medium models utilize a novel architectural design we refer to as mixture of linear experts that enables increasing model capacity without sacrificing speed. For example, UMA-medium has 1.4B parameters but only ~50M active parameters per atomic structure. We evaluate UMA models on a diverse set of applications across multiple domains and find that, remarkably, a single model without any fine-tuning can perform similarly or better than specialized models. We are releasing the UMA code, weights, and associated data to accelerate computational workflows and enable the community to continue to build increasingly capable AI models.

翻译：快速精确地计算原子模拟性质的能力对于推动化学和材料科学领域大量应用的发展至关重要，包括药物发现、能量存储和半导体制造。为满足这一需求，Meta FAIR提出了通用原子模型系列（UMA），旨在突破速度、精度与泛化能力的前沿边界。UMA模型通过整合分子、材料、催化剂等多个化学领域的数据，在五亿个独特的3D原子结构（迄今最大规模的训练数据集）上进行训练。我们建立了经验缩放定律，以帮助理解如何同步提升模型容量与数据集规模来实现最佳精度。UMA小型与中型模型采用了一种新颖的架构设计——线性专家混合机制，该设计能在不牺牲速度的前提下提升模型容量。例如，UMA-medium模型虽拥有14亿参数，但每个原子结构仅激活约5000万参数。我们在跨多个领域的多样化应用场景中对UMA模型进行评估，发现值得注意的是，未经任何微调的单一模型能够达到甚至超越专用模型的性能。我们将公开UMA的代码、权重及相关数据，以加速计算工作流程，并推动学界持续构建能力更强的人工智能模型。