预训练机器学习势在分子模拟中的准确性和效率基准
Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations
AI总结：
本文评估了15种预训练MLIPs的准确性、速度、内存使用和稳定性，发现模型参数数量和训练集大小与准确性密切相关，但显式库仑能项无明显优势。
摘要：
预训练机器学习互作用势（MLIPs）的快速发展使得在给定应用中选择最佳模型变得具有挑战性。我们基准测试了15种预训练MLIPs，评估其准确性、速度、内存使用和生成稳定模拟的能力。这为从业者选择最适合自身模拟的MLIP提供了客观依据，并揭示了影响模型准确性的关键因素。我们发现模型参数数量和训练集大小与准确性密切相关，但未观察到包含显式库仑能项的明显优势。速度和内存使用主要由模型架构和模型大小决定。
英文摘要：
The rapid development of pretrained Machine Learning Interatomic Potentials (MLIPs) that cover a wide range of molecular species has made it challenging to select the best model for a given application. We benchmark 15 pretrained MLIPs, evaluating each one on accuracy, speed, memory use, and ability to produce stable simulations. This provides an objective basis for practitioners to select the most appropriate MLIP for their own simulations, and offers insight into which factors most strongly influence model accuracy. We find that the number of model parameters and the size of the training set are both strongly correlated with accuracy, but observe no benefit from including explicit Coulomb energy terms. Speed and memory use are determined as much by the model architecture as by the size of the model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
