Accessible Open-Source Software Framework for Multi-Scale Modeling
New multi-modal nanoscale capabilities open the door for the accelerated discovery of new fundamental concepts and phenomena in energy-relevant materials science across multiple length- and time scales. There is a significant amount of complex multidimensional new data being generated as new tools are developed. This increased data requires new comprehensive digital approaches combining data- and physics-based modeling to analyze multi-dimensional correlations and enable new scientific discoveries and improved capabilities for autonomous experiments. Multiscale Modeling is used extensively by scientists worldwide to analyze, predict, and modify the behavior of materials and chemicals at the nanoscale. Global enterprises in industrial sectors of critical importance (such as energy, electronics, and chemical engineering, among others) rely on nanoscale R&D capabilities for their competitive strategy. R&D is a cost-intensive and long process, and many companies today attempt to deploy Nanoscale Modeling software to gain a competitive edge. The pace of academic research is proliferating, including many new developments from the DOE [DMREF, MGI]. Due to its complexity, however, this modeling software is yet to be accessible to the majority of scientifically capable communities. During Phase I we (1) developed the foundation of the declarative data formats (implemented using JSON, JSON schemas, and YAML) capable of storing the complex data, metadata, and associated execution logic for the multi-scale materials modeling and simulation workflows, and (2) published a proof-of-concept set of open-source libraries implementing some of the most-widely deployed first-principles workflows for electronic properties of materials, such as electronic band structure, heterostructure band offsets, vibrational properties, optical properties calculations with NWChem, VASP, and Quantum ESPRESSO, and (3) demonstrated how the open-source components can be assembled and deployed for the prediction of structural and electronic properties of materials. We demonstrated how the coupled physics-based and AI/ML-driven approaches [3] could work within a single digital platform.
- Research Organization:
- Exabyte Inc
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- DOE Contract Number:
- DE-SC0022390
- OSTI ID:
- 1957806
- Type / Phase:
- SBIR (Phase I)
- Report Number(s):
- Final Report DE-SC0022390
- Country of Publication:
- United States
- Language:
- English
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