Artificial Intelligence and Machine Learning for Materials Discovery, Synthesis and Characterization

Submission Deadline: December 9, 2024

The use of artificial intelligence, including machine learning, is rapidly rising in all areas of materials science, from materials discovery, synthesis, characterization, and performance. This special collection explores these areas and highlights successes and challenges.

Topics covered include, but are not limited to:

  • The analysis and interpretation of micrographs (Optical, SEM, TEM, etc.)
  • Analysis and interpretation of spectra and diffraction patterns (XPS, Auger, XRD, ToF-SIMS, RHEED, etc.)
  • Computational materials discovery and autonomous experimentation
  • Thin film deposition and etching process development, analysis, and control
  • Data mining in materials science
  • Device characterization, including high throughput approaches
  • Procedures and methods for training and testing models, including evaluation of test/reference data quality

Guest Editors

Parag Banerjee, University of Central Florida

Jeffrey Elam, Argonne National Laboratory

Wil Gardner, La Trobe University

Tiffany Kaspar, Pacific Northwest National Laboratory

Chris Moffitt, Kratos Analytical, Inc.

Paul Pigram, La Trobe University

Editor

Amy Walker, University of Texas at Dallas

Manuscript Details & Submission

Authors are encouraged to use the JVST article template available here. During submission, you will have an opportunity to indicate that your paper is a part of one of these collections by choosing the Special Topic Collection on “Artificial Intelligence and Machine Learning for Materials Discovery, Synthesis and Characterization.”
Submission Deadline: December 9, 2024