About this Research Topic
The development of new materials, incorporation of new functionalities in materials, and even the description of well-studied materials strongly depends on the capability to understand and predict complex structure-properties relationships. A significant challenge in this field remains the “curse of dimensionality”. Even for the characterization of materials with a rather moderate number of constituents and relevant scales, often high-dimensional parameter spaces have to be sampled. Experimental approaches to do so are usually limited by their costs and processing time.
Therefore, computational approaches receive increasing attention, but there is still a long way to go until digital twins are sufficiently predictive and computational efficient to replace the experiment. Virtual materials development is an emerging field that aims at the provision of such digital twins that can be connected in a modular library in powerful workflows. Such workflows need to combine different computational methods for different scales such as DFT calculations on the quantum level, MD simulations on the atomistic level and FEM on the continuum scale, and have to run on a wide spectrum of computer applications from mobile phone to high-performance computers. For effective scale-bridging simulations and parameter studies in multidimensional spaces, the simulations on each level still require substantial speed-ups, sometimes even by orders of magnitude. One way to realize this is to replace numerical models solving systems of differential equations by machine-learning-based surrogate models.
This Research Topic collects current ideas and novel concepts for the advancement of virtual materials design with a focus on materials acceleration and digital twins. Of primary interest are modules for digital twins using the above-mentioned methods, such as DFT, MD, and FEM as well as workflows to accelerate materials discovery by the integration of such modules. These workflows can also be used for the generation of data, which can be analyzed by machine learning and other data driven-approaches to accelerate the design of new materials and materials processing. Both papers on general methods as well as their application to decoding the complex relationships along the chain composition - processing - structure – properties are highly welcome. The replacement of experiments with validated and quantitatively predictive computational methods, the identification of dependencies and mechanisms from big data, as well as the representation of high-dimensional relationships by computer models are of particular relevance.
Topics of interest include, but are not limited to:
• Integration of DFT, MD or FEM in high-throughput simulation environments
• Automated data generation from experiments or computational methods for decoding hidden information
• Data mining, machine learning, artificial neural networks, data-driven computing
• Design, validation, storage and re-use of Workflows in libraries
• Applications in form of digital twins, covering property prediction, scale bridging, optimization, materials design
Therefore, computational approaches receive increasing attention, but there is still a long way to go until digital twins are sufficiently predictive and computational efficient to replace the experiment. Virtual materials development is an emerging field that aims at the provision of such digital twins that can be connected in a modular library in powerful workflows. Such workflows need to combine different computational methods for different scales such as DFT calculations on the quantum level, MD simulations on the atomistic level and FEM on the continuum scale, and have to run on a wide spectrum of computer applications from mobile phone to high-performance computers. For effective scale-bridging simulations and parameter studies in multidimensional spaces, the simulations on each level still require substantial speed-ups, sometimes even by orders of magnitude. One way to realize this is to replace numerical models solving systems of differential equations by machine-learning-based surrogate models.
This Research Topic collects current ideas and novel concepts for the advancement of virtual materials design with a focus on materials acceleration and digital twins. Of primary interest are modules for digital twins using the above-mentioned methods, such as DFT, MD, and FEM as well as workflows to accelerate materials discovery by the integration of such modules. These workflows can also be used for the generation of data, which can be analyzed by machine learning and other data driven-approaches to accelerate the design of new materials and materials processing. Both papers on general methods as well as their application to decoding the complex relationships along the chain composition - processing - structure – properties are highly welcome. The replacement of experiments with validated and quantitatively predictive computational methods, the identification of dependencies and mechanisms from big data, as well as the representation of high-dimensional relationships by computer models are of particular relevance.
Topics of interest include, but are not limited to:
• Integration of DFT, MD or FEM in high-throughput simulation environments
• Automated data generation from experiments or computational methods for decoding hidden information
• Data mining, machine learning, artificial neural networks, data-driven computing
• Design, validation, storage and re-use of Workflows in libraries
• Applications in form of digital twins, covering property prediction, scale bridging, optimization, materials design
Keywords: Virtual Materials Development, Digital Twins, Workflows and Libraries, Scale bridging, Machining of data, Machine learning, Data mining
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
