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Dynamic Workflows for Routine Materials Discovery in Surface Science.

Kevin Tran1, Aini Palizhati1, Seoin Back1

  • 1Department of Chemical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15217 , United States.

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Informatics and data science accelerate materials discovery. A new tool using dynamic dependency graphs enhances surface science research, particularly for catalyst screening.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Catalysis

Background:

  • Informatics and data science have transformed inorganic crystal and small molecule studies, enabling accurate machine learning models for structure/property relationships.
  • The application of these tools in catalysis has lagged due to complex calculation requirements and large configuration spaces.

Purpose of the Study:

  • To discuss the role of informatics in accelerating research and review existing workflows, databases, and surrogate models.
  • To introduce a novel informatics tool designed to overcome challenges in catalysis research by managing complex calculations.
  • To demonstrate the tool's utility in screening intermetallic surfaces for electrochemical catalyst activity.

Main Methods:

  • Development of an informatics tool utilizing dynamic dependency graphs for organizing and scheduling calculations.
  • Application of the tool to large-scale screening of intermetallic surfaces for electrochemical catalysis.
  • Review of literature on informatics workflows, databases, and surrogate models in materials science.

Main Results:

  • The informatics tool facilitates flexible research workflows in surface science by managing interdependent calculations.
  • Successful application of the tool for high-throughput screening of intermetallic catalysts.
  • Demonstration of how informatics can be effectively integrated into surface science research.

Conclusions:

  • Informatics and data science offer significant potential to advance surface science research.
  • Dynamic dependency graphs provide a flexible framework for complex computational workflows in catalysis.
  • Adoption of similar informatics approaches is crucial for the catalysis community to leverage data science benefits.