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Whitman, S.E. and Latypov, M.I. (2025) Machine Learning of Microstructure-Property Relationships in Materials Leveraging Microstructure Representation from Foundational Vision Transformers. Acta Materialia, 296, Article ID: 121217.
https://doi.org/10.1016/j.actamat.2025.121217

has been cited by the following article:

• TITLE: Communication: Phase Diagram, Microstructure and Atomistic 2nd Law of Thermodynamics

  AUTHORS: Kuochung Liu

  KEYWORDS: Phase Diagram, Atomistic 2nd Law, Chemical Potential, Microstructure, Property, AI, T-T-T Curve, Driving Force, Mobility, Slags

  JOURNAL NAME: Advances in Materials Physics and Chemistry, Vol.16 No.6, June 5, 2026

  ABSTRACT: The Atomistic 2nd Law of Thermodynamics and its necessary and sufficient condition can be used to explain the phase diagrams of one or two components. In these explanations, the lowest molar total chemical potential of atomistic features plays an important role, while the common tangent and lever rule are useful in understanding binary phase diagrams. In explaining the microstructural evolution at a composition of a binary phase diagram, the driving force, the mobility of atomistic features and lever rule etc. can be well applied. Recently the use of machines or AI in the optimization of material properties is highly valued. More effective results could be obtained if the users of these tools have better understanding of the Atomistic 2nd Law of Thermodynamics and kinetics. The discussed topics are: T-T-T curves and phase diagrams, T-T-T curves of 1080 steel, T-T-T curves of other materials, systems containing low-melting impurities.