Abstract
AbstractIn recent years, modeling and simulation of materials have become indispensable to complement experiments in materials design. High‐throughput simulations increasingly aid researchers in selecting the most promising materials for experimental studies or by providing insights inaccessible by experiment. However, this often requires multiple simulation tools to meet the modeling goal. As a result, methods and tools are needed to enable extensive‐scale simulations with streamlined execution of all tasks within a complex simulation protocol, including the transfer and adaptation of data between calculations. These methods should allow rapid prototyping of new protocols and proper documentation of the process. Here an overview of the benefits and challenges of workflow engineering in virtual material design is presented. Furthermore, a selection of prominent scientific workflow frameworks used for the research in the BATTERY 2030+ project is presented. Their strengths and weaknesses as well as a selection of use cases in which workflow frameworks significantly contributed to the respective studies are discussed.
Highlights
Introduction the underlying taskThe computational challenges for understanding the material properties encompass interdisciplinaryMaterials with tailored properties are an essential basis for the research, where the comprehension of its nature runs through development of new technological solutions in the fields of different scales of materials behavior, requiring multi-scale energy and environment, health, information and communica- approaches
We present an overview of a few representative workflow environments
For example the Materials Studio Collection (MSC) provides components and protocols that utilize the functionality of BIOVIA Materials Studio, covering straight forward access to structure builders and symmetry functions, classical molecular dynamics (MD), mesoscale simulations, density-functional theory (DFT) calculations, as well as analysis components for all of these functions
Summary
Scientific workflows can be viewed as an approach that models computational tasks in simulation and data analysis to understand the physical nature of complex systems. A workflow represents the coordinated execution of repeatable computational steps while accounting for dependencies and concurrency of tasks. The design and study of digital twins does not necessarily require a workflow framework. By providing a layer of abstraction, workflows enable scientists to design and conduct studies without in-depth knowledge of the software deployment on computational resources. They improve the transfer of knowledge within groups, collaborators, or users by providing a concise description of the input data, utilized software and scripts, and the respective parameters and settings used for a specific project. Some workflow systems support the execution of process steps on different computing resources
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