  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.
