Publications
Found 14 results
Author [ Title] Type Year Filters: First Letter Of Title is M and Author is A. Van der Ven [Clear All Filters]
MultiShifter: Software to generate structural models of exended two-dimensional defects in 3D and 2D crystals." Computational Materials Science 191 (2021).
"Multiphysics Simulations of Lithiation-Induced Stress in Li1+ x Ti2O4 Electrode Particles." The Journal of Physical Chemistry C 120 (2016): 27871-27881.
"Multielectron, Cation and Anion Redox in Lithium-Rich Iron Sulfide Cathodes." Journal of the American Chemical Society 142, no. 14 (2020).
"Modeling magnetic evoluton and exchange hardening in disordered magnets: The example of Mn1-xFexRu2Sn Heusler alloys." Physical Review Materials 3 (2019).
"Mg Intercalation in Layered and Spinel Host Crystal Structures for Mg Batteries." Inorganic chemistry 54 (2015): 4394-4402.
"Mesoporous TiO 2–B microflowers composed of (1 1 [combining macron] 0) facet-exposed nanosheets for fast reversible lithium-ion storage." Journal of Materials Chemistry A 1 (2013): 12028-12032.
"Mechanochemical spinodal decomposition: a phenomenological theory of phase transformations in multi-component, crystalline solids." npj Computational Materials 2 (2016): 16012.
"Mechanical instabilities and structural phase transitions: The cubic to tetragonal transformation." Acta Materialia 56 (2008): 4226-4232.
"Mapping skyrmion stability in uniaxial lacunar spinel magnets from first principles." Physical Review B 101 (2020).
"Manganese oxidation as the origin of the anomalous capacity of Mn-containing Li-excess cathode materials." Nature Energy (2019).
"Main-Group Halide Semiconductors Derived from Perovskite: Distinguishing Chemical, Structural, and Electronic Aspects." Inorganic Chemistry 56 (2017): 11-25.
"Machine-learning the configurational energy of multicomponent crystalline solids." npj Computational Materials 4 (2018).
"Machine learning the density functional theory potential energy surface for the inorganic halide perovskite CsPbBr3." Physical Review B 100 (2019).
"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).
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