This project tackles the data challenge of data analysis of molecular dynamics simulations on the next-generation supercomputers by:
- Creating new in situ methods to trace molecular events such as conformational changes, phase transitions, or binding events in molecular dynamics simulations at runtime by locally reducing knowledge on high-dimensional molecular organization into a set of relevant structural molecular properties
- Integrating simulation and analytics into complex workflows for runtime detection of changes in structural and temporal molecular properties
- Designing new data representations and extend unsupervised machine learning techniques to accurately and efficiently build an explicit global organization of structural and temporal molecular properties
- Developing new curriculum material, online courses, and online training material targeting data analytics
The project's harnessed knowledge of molecular structures' transformations at runtime can be used to steer simulations to more promising areas of the simulation space, identify the data that should be written to congested parallel file systems, and index generated data for retrieval and post-simulation analysis. Supported by this knowledge, molecular dynamics workflows such as replica exchange simulations, Markov state models, and the string method with swarms of trajectories can be executed from the outside (i.e., without reengineering the molecular dynamics code)
Full Changelog: https://github.com/Analytics4MD/A4MD/commits/V1.0