Research Topic

Data-driven Design of Catalytic Materials

About this Research Topic

Data-driven materials design is rapidly becoming a major thrust in science and technology. The growing ability to produce large amounts of reliable and consistent theoretical and experimental data created new opportunities in using the data to gain understanding and uncover hidden correlations between material's properties of different levels of complexity. This is particularly helpful in the area of catalysis, where the relationship between target properties such as turnover frequency of a catalytic reaction and the material emerges as a result of interplay of processes with spatial and temporal scales spanning more than ten orders of magnitude. Data analytics with the help of artificial intelligence (AI) can play a pivotal role in bridging the multiple scales and establishing the complex relation between basic, easily obtainable features of a catalytic material on one hand, and its stability, catalytic activity, and selectivity on the other. To achieve this, three main ingredients are necessary: (i) reliable and consistent theoretical and experimental data, (ii) development of domain-specific data-analytics methods, and (iii) high-throughput screening of materials using AI models. These ingredients and their combinations will be the focus of the article collection.


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.

Data-driven materials design is rapidly becoming a major thrust in science and technology. The growing ability to produce large amounts of reliable and consistent theoretical and experimental data created new opportunities in using the data to gain understanding and uncover hidden correlations between material's properties of different levels of complexity. This is particularly helpful in the area of catalysis, where the relationship between target properties such as turnover frequency of a catalytic reaction and the material emerges as a result of interplay of processes with spatial and temporal scales spanning more than ten orders of magnitude. Data analytics with the help of artificial intelligence (AI) can play a pivotal role in bridging the multiple scales and establishing the complex relation between basic, easily obtainable features of a catalytic material on one hand, and its stability, catalytic activity, and selectivity on the other. To achieve this, three main ingredients are necessary: (i) reliable and consistent theoretical and experimental data, (ii) development of domain-specific data-analytics methods, and (iii) high-throughput screening of materials using AI models. These ingredients and their combinations will be the focus of the article collection.


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

30 June 2021 Abstract
30 December 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

30 June 2021 Abstract
30 December 2021 Manuscript

Participating Journals

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

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