Open-Source high-throughput solvation and simulation of explicitly solvated systems

The accurate solvation of molecules with an explicit solvent can be challenging. To make this easier, I codeveloped Autosolvate, an open-source package which allows high-throughput solvation and equilibration of organic systems. In individual steps the systems are generated, pressure-equilibrated, configurationally sampled with QM/MM, and then the clusters of center molecule + solvent shells are extracted. Documentation at https://autosolvate.readthedocs.io/. [Hruska et al., Autosolvate: A Toolkit for Automating Quantum Chemistry design and Discovery of Solvated Molecules, 2022]

Open-Source Software for Execution of complex sampling workflows on 2000+ GPUs

scaling to 2000 GPUs

Running adaptive sampling strategies as described in the Research section requires efficient, scalable, and user-friendly execution of both the molecular dynamics and the iterative approach. I led the development of version 2 of the ExTASY software optimized for adaptive sampling. The code is available at https://github.com/ClementiGroup/ExTASY/. The modular python code allows an user-friendly swap of the molecular dynamics packages or the particular sampling methods with little code change. The Python-based interface to the interoperable and high-performance pilot-based run time system allows the scalability of ExTASY on supercomputers to reach 2000+ concurrent GPUs or concurrent molecular dynamics simulations. [Hruska et al., Extensible and scalable adaptive sampling on supercomputers., 2020] [Balasubramanian et al., Extasy: Scalable and flexible coupling of MD simulations and advanced sampling techniques, 2016]

The high scalability of the ExTASY package is enabled by asynchronous execution. The diagram illustrates that in the synchronous case up to 1000s of GPUs have to wait until the analysis step finishes. In contrast, in an asynchronous workflow the GPUs/nodes don't have to wait, significantly increasing the scalability of this heterogeneous use case. [Hruska et al., Extensible and scalable adaptive sampling on supercomputers., 2020]