标题： 用于液体和材料性质的高效长程机器学习力场 显示英文标题

标题： Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties

摘要： 机器学习力场（MLFFs）已成为一种先进的工具，用于成本高效的原子模拟，接近DFT精度，最近的消息传递MLFFs能够覆盖整个周期表。 我们提出了一种不变的消息传递MLFF架构（MPNICE），该架构迭代地预测原子部分电荷，包括长程相互作用，能够在实现与具有可比精度的模型相当的5-20倍更快的推理速度的同时，预测依赖电荷的性质。 我们为有机系统训练了直接和delta学习的MPNICE模型，并与液体和固体系统的实验性质进行对比测试。 我们还对有限系统的能量进行了对比测试，贡献了一个新的带有带电物种的扭转扫描集，以及一个用于TorsionNet500基准的新DLPNO-CCSD(T)参考集。 我们还训练并测试了用于块体无机晶体的MPNICE模型，重点在于结构排序和机械性能。 最后，我们探索了无机和有机系统的多任务模型，这些模型在特定领域任务上的表现略有下降，但表现出令人惊讶的泛化能力，稳定地预测了$\simeq500$Pt/Ir有机金属配合物的气相结构，尽管从未针对任何类型的有机金属配合物进行过训练。 显示英文摘要

摘要： Machine learning force fields (MLFFs) have emerged as a sophisticated tool for cost-efficient atomistic simulations approaching DFT accuracy, with recent message passing MLFFs able to cover the entire periodic table. We present an invariant message passing MLFF architecture (MPNICE) which iteratively predicts atomic partial charges, including long-range interactions, enabling the prediction of charge-dependent properties while achieving 5-20x faster inference versus models with comparable accuracy. We train direct and delta-learned MPNICE models for organic systems, and benchmark against experimental properties of liquid and solid systems. We also benchmark the energetics of finite systems, contributing a new set of torsion scans with charged species and a new set of DLPNO-CCSD(T) references for the TorsionNet500 benchmark. We additionally train and benchmark MPNICE models for bulk inorganic crystals, focusing on structural ranking and mechanical properties. Finally, we explore multi-task models for both inorganic and organic systems, which exhibit slightly decreased performance on domain-specific tasks but surprising generalization, stably predicting the gas phase structure of $\simeq500$ Pt/Ir organometallic complexes despite never training to organometallic complexes of any kind.