Research Topic

Data-Driven Low-Dimensional Materials Development

About this Research Topic

Low-dimensional (LD) materials such as nanoparticles, nanotubes, and two-dimensional thin films have attracted tremendous interest driven by new physics that arises from the reduced dimensionality, motivating the continuous search for novel LD materials and structures. Compared to their 3D counterparts, LD materials exhibit unique electronic, optical, chemical, mechanical, and thermal properties which can be tailored through their chemical compositions, morphologies, and structures. LD materials, therefore, provide enormous opportunities in various applications including energy storage and conversion, environmental pollution control, electronic and optoelectronic devices, communications, imaging, and sensing.

Yet, the increasing interest has not been fully translated into the reality of rapid development due to the enormous complexity involved in rich material chemistry, multi-variable synthesis methods, repetitive experimental characterizations, and numerous applications. Moreover, traditional experiments and computational modeling often consume tremendous time and resources and are limited by experimental conditions and theoretical foundations. Therefore, it is imperative to develop new methods to accelerate the discovery, design, synthesis, and characterization process for LD materials and their applications. Recently, data-driven approaches with the intent to accelerate the discovery of new LD materials or improved materials properties have received increasing attention and have achieved great improvements in both time efficiency and prediction accuracy.

In this Research Topic, the frontiers in LD materials development will be described and reviewed with an emphasis on data-driven approaches. We invite submission of research articles covering the following topics:

· Machine learning assisted LD materials discovery and design

· Data-driven approaches for LD materials production and process optimization

· Data fusion for process scale-up

· Data analytics for structural and functional imaging

· Physical model and experimental system uncertainty quantification

· Application demonstration of newly-developed data-driven LD materials


Keywords: low-dimensional materials, data-driven approaches, design and synthesis, machine learning, structural and functional imaging


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.

Low-dimensional (LD) materials such as nanoparticles, nanotubes, and two-dimensional thin films have attracted tremendous interest driven by new physics that arises from the reduced dimensionality, motivating the continuous search for novel LD materials and structures. Compared to their 3D counterparts, LD materials exhibit unique electronic, optical, chemical, mechanical, and thermal properties which can be tailored through their chemical compositions, morphologies, and structures. LD materials, therefore, provide enormous opportunities in various applications including energy storage and conversion, environmental pollution control, electronic and optoelectronic devices, communications, imaging, and sensing.

Yet, the increasing interest has not been fully translated into the reality of rapid development due to the enormous complexity involved in rich material chemistry, multi-variable synthesis methods, repetitive experimental characterizations, and numerous applications. Moreover, traditional experiments and computational modeling often consume tremendous time and resources and are limited by experimental conditions and theoretical foundations. Therefore, it is imperative to develop new methods to accelerate the discovery, design, synthesis, and characterization process for LD materials and their applications. Recently, data-driven approaches with the intent to accelerate the discovery of new LD materials or improved materials properties have received increasing attention and have achieved great improvements in both time efficiency and prediction accuracy.

In this Research Topic, the frontiers in LD materials development will be described and reviewed with an emphasis on data-driven approaches. We invite submission of research articles covering the following topics:

· Machine learning assisted LD materials discovery and design

· Data-driven approaches for LD materials production and process optimization

· Data fusion for process scale-up

· Data analytics for structural and functional imaging

· Physical model and experimental system uncertainty quantification

· Application demonstration of newly-developed data-driven LD materials


Keywords: low-dimensional materials, data-driven approaches, design and synthesis, machine learning, structural and functional imaging


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.

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Submission Deadlines

13 May 2021 Abstract
29 August 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

13 May 2021 Abstract
29 August 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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