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.

翻译：分子动力学模拟已成为研究生物分子的基础工具。同时，在分子可发生波动的多种条件下对粒子集合进行模拟具有重要应用价值。本文探索并改进了软提示学习方法在分子动力学任务中的应用。我们的模型能够在有限训练数据下显著泛化至未见及分布外场景。尽管本工作以温度作为测试案例，但该方法的多功能性使其能够通过任意连续动态条件（如压力与体积）实现高效模拟。该框架包含两个阶段：1) 采用数据混叠技术进行预训练，增强分子结构数据与温度提示，并通过逐步提升其比例应用课程学习策略；2) 基于元学习的微调框架提升了微调过程的样本效率，并为软提示调优提供更优的初始点。全面实验表明，我们的框架在域内数据上具有卓越的准确性，并在未见及分布外样本上展现出强大的泛化能力。