# Molecular Dynamics Simulations on Cloud Computing and Machine Learning Platforms

arXiv: https://arxiv.org/abs/2111.06466 · HTML: https://mlsanity.com/paper/2111.06466/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 11 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Machine Learning of coarse-grained Molecular Dynamics Force Fields](https://mlsanity.com/paper/1812.01736/) (2018)
- [TorchMD: A deep learning framework for molecular simulations](https://mlsanity.com/paper/2012.12106/) (2020)
- [HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges](https://mlsanity.com/paper/1710.08731/) (2017)

## Abstract (arXiv)

Scientific computing applications have benefited greatly from high performance computing infrastructure such as supercomputers. However, we are seeing a paradigm shift in the computational structure, design, and requirements of these applications. Increasingly, data-driven and machine learning approaches are being used to support, speed-up, and enhance scientific computing applications, especially molecular dynamics simulations. Concurrently, cloud computing platforms are increasingly appealing for scientific computing, providing "infinite" computing powers, easier programming and deployment models, and access to computing accelerators such as TPUs (Tensor Processing Units). This confluence of machine learning (ML) and cloud computing represents exciting opportunities for cloud and systems researchers. ML-assisted molecular dynamics simulations are a new class of workload, and exhibit unique computational patterns. These simulations present new challenges for low-cost and high-performance execution. We argue that transient cloud resources, such as low-cost preemptible cloud VMs, can be a viable platform for this new workload. Finally, we present some low-hanging fruits and long-term challenges in cloud resource management, and the integration of molecular dynamics simulations into ML platforms (such as TensorFlow).