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Materials Informatics: Statistical Modeling in Material Science.

Abraham Yosipof1,2, Klimentiy Shimanovich3,4, Hanoch Senderowitz3

  • 1Department of Business Administration, Peres Academic Center, Rehovot, 76102, Israel.

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|November 22, 2016
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Summary
This summary is machine-generated.

Material informatics accelerates new material discovery using data mining and machine learning (Quantitative Structure Activity Relationship modeling). This study compares its use in drug design versus materials science, focusing on developing better material-specific descriptors for energetic materials and solar cells.

Keywords:
Material informaticsQSARchemoinformaticsmaterial sciencephotovoltaic cellssolar cells

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Area of Science:

  • Materials Science
  • Informatics
  • Computational Chemistry

Background:

  • Material informatics applies computational principles to materials science for accelerated discovery and development.
  • Quantitative Structure Activity Relationship (QSAR) modeling, a machine learning approach, is central to deriving predictive models for material properties.
  • Existing QSAR models often originate from medicinal chemistry and may require adaptation for materials science applications.

Purpose of the Study:

  • To compare QSAR modeling in medicinal chemistry/drug design with its application in materials science.
  • To emphasize the need for novel, materials-specific descriptors.
  • To review recent QSAR models in materials science, particularly for energetic materials and solar cells.

Main Methods:

  • Application of data mining and machine learning techniques for predictive modeling.
  • Development and utilization of Quantitative Structure Activity Relationship (QSAR) models.
  • Analysis of combinatorial material synthesis libraries, specifically metal oxide solar cells.

Main Results:

  • Identification of key factors governing material properties through predictive models.
  • Survey of recent QSAR models applied to energetic materials and solar cells.
  • Material-informatic analysis of solar cell libraries revealing physical insights and guiding the design of improved photovoltaic devices.

Conclusions:

  • QSAR modeling is a powerful tool for accelerating materials discovery and development.
  • Tailoring descriptors for materials science applications is crucial for effective QSAR modeling.
  • This approach offers significant potential for designing next-generation solar cells with enhanced performance.