标题： 教师-学生训练提高了机器学习原子间势的准确性和效率 显示英文标题

标题： Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials

摘要： 机器学习间歇原子势（MLIPs）正在彻底改变分子动力学（MD）模拟领域。最近的MLIPs倾向于在更大数据集上训练更复杂的架构。由此导致的计算和内存成本增加可能会阻碍这些MLIPs在大规模MD模拟中的应用。在这里，我们提出了一种教师-学生训练框架，其中教师（原子能量）的潜在知识被用来增强学生的训练。我们展示了轻量级的学生MLIPs相比教师模型，在内存占用极小的情况下具有更快的MD速度。令人惊讶的是，即使学生模型和教师模型都训练自相同的量子化学数据集，学生模型的准确性甚至可以超过教师模型。我们的工作强调了MLIPs用于减少大规模MD模拟所需资源的一种实用方法。 显示英文摘要

摘要： Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures trained on larger datasets. The resulting increase in computational and memory costs may prohibit the application of these MLIPs to perform large-scale MD simulations. Here, we present a teacher-student training framework in which the latent knowledge from the teacher (atomic energies) is used to augment the students' training. We show that the light-weight student MLIPs have faster MD speeds at a fraction of the memory footprint compared to the teacher models. Remarkably, the student models can even surpass the accuracy of the teachers, even though both are trained on the same quantum chemistry dataset. Our work highlights a practical method for MLIPs to reduce the resources required for large-scale MD simulations.