Publications
Found 28 results
Author Title [ Type
] Year Filters: First Letter Of Last Name is N [Clear All Filters]
"Cost-sensitive experimental design for atomistic modeling." In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World., 2022.
"CASM—A software package for first-principles based study of multicomponent crystalline solids." Computational Materials Science 217 (2023): 111897.
"Comparing crystal structures with symmetry and geometry." npj Computational Materials 7 (2021): 164.
"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.
"Connecting the Simpler Structures to Topologically Close-Packed Phases." Physical Review Letters 121 (2018).
"Crystallography and substitution patterns in the Zr O 2 − YTa O 4 system." Physical Review Materials 2 (2018).
"Crystallography, thermodynamics and phase transitions in refractory binary alloys." Acta Materialia 200 (2020).
"Discovering hierarchies among intermetallic crystal structures." Physical Review Materials 4 (2020).
"On the early stages of precipitation in dilute Mg–Nd alloys." Acta Materialia 108 (2016): 367-379.
"Effects of strain on the stability of tetragonal ZrO 2." Physical Review B 94 (2016): 054108.
"Electrochemical Oxidative Fluorination of an Oxide Perovskite." Chemistry of Materials 33 (2021): 5757-5768.
"First-principles investigation of phase stability in the Mg-Sc binary alloy." Physical Review B 95 (2017): 214107.
"First-principles statistical mechanics of multicomponent crystals." Annual Review of Materials Research 48 (2018).
"Ionic conduction in cubic Na3TiP3O9N, a secondary Na-ion battery cathode with extremely low volume change." Chemistry of Materials 26 (2014): 3295-3305.
"Linking electronic structure calculations to generalized stacking fault energies in multicomponent alloys." NPJ Computational Materials 6 (2020): 80.
"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).
"Machine learning the density functional theory potential energy surface for the inorganic halide perovskite CsPbBr3." Physical Review B 100 (2019).
"Machine-learning the configurational energy of multicomponent crystalline solids." npj Computational Materials 4 (2018).
"Misfit-driven beta'''precipitate composition and morphology in Mg-Nd alloys." Acta Materialia 136 (2017).
"Order parameters for antiferromagnetic structures: A first-principles study of iridium manganese." Physical Review Materials 6 (2022): 044402.
"Partitioning of Ca to metastable precipitates in a Mg-rare earth alloy." Materials Research Letters 11 (2023): 222-230.
"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).
"Six new transformation pathways connecting simple crystal structures and common intermetallic crystal structures." Acta Materialia 221 (2021): 117429.
"Stability and strain-driven evolution of β′ precipitate in Mg-Y alloys." Acta Materialia 166 (2019).
"Symmetry-adapted order parameters and free energies for solids undergoing order-disorder phase transitions." Physical Review B 96 (2017): 134204.