Integrated workflows and interfaces for data-driven semi-empirical electronic structure calculations

Abstract

Modern software engineering of electronic structure codes has seen a paradigm shift from monolithic workflows towards object-based modularity. Software objectivity allows for greater flexibility in the application of electronic structure calculations, with particular benefits when integrated with approaches for data-driven analysis. Here, we discuss different approaches to create "deep" modular interfaces that connect big-data workflows and electronic structure codes, and explore the diversity of use cases that they can enable. We present two such interface approaches for the semi-empirical electronic structure package, DFTB+. In one case, DFTB+ is applied as a library and provides data to an external workflow; and in another, DFTB+ receives data via external bindings and processes the information subsequently within an internal workflow. We provide a general framework to enable data exchange workflows for embedding new machine-learning-based Hamiltonians within DFTB+, or to enabling deep integration of DFTB+ in multiscale embedding workflows. These modular interfaces demonstrate opportunities in emergent software and workflows to accelerate scientific discovery by harnessing existing software capabilities.

12 pages, 5 figures, Journal of Chemical Physics special issue "modular and interoperable software for chemical physics"

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• Machine Learning and Data-Driven Methods in Computational Surface and Interface Science
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