Machine learning for predicting occurrence of interphase precipitation in HSLA steels

This publication has 39 references indexed in Scilit:

• Three-dimensional atom probe microscopy study of interphase precipitation and nanoclusters in thermomechanically treated titanium–molybdenum steels
  Acta Materialia, 2013
• A supervised clustering approach for fMRI-based inference of brain states
  Pattern Recognition, 2012
• Effects of Ferrite Growth Rate on Interphase Boundary Precipitation in V Microalloyed Steels
  ISIJ International, 2012
• Stability of (Ti, M)C (M = Nb, V, Mo and W) carbide in steels using first-principles calculations
  Acta Materialia, 2012
• A model for interphase precipitation based on finite interface solute drag theory
  Acta Materialia, 2010
• Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
  Chemistry of Materials, 2010
• PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data
  Neuroinformatics, 2009
• A general method for calculating deviation from local equilibrium at phase interfaces
  Acta Materialia, 2003
• Effect of alloying elements on the γ to α transformation in steel. I
  Acta Materialia, 2002
• Precipitation of carbides at γ─α boundaries in alloy steels
  Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 1971