A comparison of Redlich-Kister polynomial and cubic spline representations of the chemical potential in phase field computations." Computational Materials Science 128 (2017): 127-139."
Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions." Computer Methods in Applied Mechanics and Engineering 353, no. 15 (2019)."
Mechanochemical spinodal decomposition: a phenomenological theory of phase transformations in multi-component, crystalline solids." npj Computational Materials 2 (2016): 16012."
Multiphysics Simulations of Lithiation-Induced Stress in Li1+ x Ti2O4 Electrode Particles." The Journal of Physical Chemistry C 120 (2016): 27871-27881."
The Role of Coherency Strains on Phase Stability in Li x FePO4: Needle Crystallites Minimize Coherency Strain and Overpotential." Journal of the Electrochemical Society 156 (2009): A949-A957."
Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys." Computer Methods in Applied Mechanics and Engineering 371 (2020)."
Three-dimensional isogeometric solutions to general boundary value problems of Toupin’s gradient elasticity theory at finite strains." Computer Methods in Applied Mechanics and Engineering 278 (2014): 705-728."