An Integrated Software Package for Studying Structure-Property-Processing Relationships in Material Systems / Topic 14A

The identification of structure-property-processing relationships require dynamical models that can access multitude of length spanning nanometers to microns and timescales spanning picoseconds to seconds. Despite its widespread availability of a variety of open source and commercial codes as well as their usage in various flavors, the predictive power of molecular dynamics (MD) is severely limited. Each flavor of MD therefore has a ceiling limit, which severely impedes its predictive power. Sentient proposes multi-fidelity scale bridging framework that provide users with the capabilities to train and develop their own classical atomistic and coarse-grained interatomic potentials (force fields) for molecular simulations. The framework will combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials by merging and exploiting the best insights from the fields of machine learning (ML), advanced optimization, and atomistic simulations. The multiscale framework will seamlessly and efficiently describe material properties and dynamical phenomena for broad class of materials at atomistic resolution over mesoscopic time and length scales without compromising the accuracy. In phase 1, Sentient will demonstrate a software package, that incorporates model development, molecular simulations, and data analysis in one user-friendly package, providing extremely rapid in-silico design and optimization capability and as such is expected to accelerate the technological advancement and industrial manufacturing through materials design and process optimization. The proposed software enables end users to develop accurate materials models across multiple length/timescales for a wide range of materials. The framework will be used for identifying structure- property-processing relationships in various material classes at an unprecedented pace; a successful implementation, and commercialization of this tool will drastically reduce the financial costs, and time required for developing new materials for emerging platforms in numerous facets of technology. Selected applications for the software include but not limited to: energy storage, batteries, quantum computing, semiconductors, polymers, advanced lubricants, rocket fuels