Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of particles under various conditions in which the molecules can fluctuate. In this paper, we explore and adapt the soft prompt-based learning method to molecular dynamics tasks. Our model can remarkably generalize to unseen and out-of-distribution scenarios with limited training data. While our work focuses on temperature as a test case, the versatility of our approach allows for efficient simulation through any continuous dynamic conditions, such as pressure and volumes. Our framework has two stages: 1) Pre-trains with data mixing technique, augments molecular structure data and temperature prompts, then applies a curriculum learning method by increasing the ratio of them smoothly. 2) Meta-learning-based fine-tuning framework improves sample-efficiency of fine-tuning process and gives the soft prompt-tuning better initialization points. Comprehensive experiments reveal that our framework excels in accuracy for in-domain data and demonstrates strong generalization capabilities for unseen and out-of-distribution samples.

本文基于软提示学习方法，探索和应用分子动力学任务的模型，能够在受限的训练数据下显著泛化到未知和分布外情境，尤其关注以温度为测试案例，同时适用于压力和体积等连续动力学条件的高效模拟。两阶段的框架通过数据混合技术预先训练、温度提示，接着采用课程学习方法增加它们的比例平滑地，再通过基于元学习的微调框架提高微调过程的样本效率，给予软提示微调更好的初始化点。综合实验证明，该框架在领域内数据的准确性上表现出色，并展现了对未知和分布外样本的强大泛化能力。

混合增强元学习用于高效样本微调蛋白质模拟器