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Machine Learning for Predicting Material Properties, Design, and Discovery of New Materials

Hongkyu Yoon, Sandia National Laboratories
Pania Newell, The University of Utah
Azadeh Sheidaei, Iowa State University
Mohammad Saber Hashemi, Iowa State University
Artificial intelligence machine learning (AI/ML) methods have been used to accelerate the prediction of material properties as well as the discovery of new materials. Manufacturing and/or natural processes to make materials with different compositions result in microstructural arrangements, textural orientations, and defects that all affect the properties and behavior of materials. Traditional methods often involve a large design and testing matrix to explore material with desired properties through intensive experiments and computational simulations. AI/ ML methods can potentially transform the traditional practice of predicting material properties and discovery faster and more accurately.
This mini symposium invites scientific and engineering contributions to the field of ML and materials sciences, including but not limited to:
  1. Recent advances in ML algorithms for predicting material properties and discovery of new materials
  2. ML in characterization of microstructure
  3. ML in designing robust materials with tailored properties
  4. ML and homogenization
  5. Integration of numerical model and ML to predict material properties.
We specifically invite participation from undergraduate and graduate students, postdocs, early careers, minorities, etc.