  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).
