P. Ganesh

Surface, Interface, and Temperature E ff ects on the Phase Separation and Nanoparticle Self Assembly of Bi-Metallic Ni0.5Ag0.5: A Molecular Dynamics Study

Origin and Reduction of Coercive Fields in ZnO-based Wurtzite Ferroelectrics

Wurtzite ferroelectrics offer a route to semiconductor-compatible non-volatile devices, but their large coercive fields (E$_c$) constrain the operating window between polarization reversal, leakage and dielectric breakdown. Atomically localized and non-classical switching fronts have recently been identified in wurtzit...

Strain Tuning the Kinetics of Topotactic Phase Transitions in Epitaxial Oxide Films

We report the kinetics of the topotactic brownmillerite–perovskite phase transition in epitaxial strontium cobaltite (SrCoOx) thin films. Our findings reveal asymmetric switching kinetics between the oxygen‐deficient SrCoO2.5 and oxygen‐rich SrCoO3 phases, driven by the epitaxial strain state. Using phase‐field modelin...

Origin of Flat Bands and Role of Electron Correlation in Lutetium Hydrides

Lutetium hydrides (LuH$_x$, $1.75 \leq x \leq 3$) form a diverse series of phases, several of which superconduct under pressure. Characterizing their electronic properties has remained challenging owing to a high propensity for hydrogen defect formation, and recent angle-resolved photoemission (ARPES) measurements reve...

Reinforcement Learning on the Discrete Composition Channel of a Crystal Generator: Validated Gains and Reward Hacking

Inverse materials design is a long-standing goal of computational materials discovery. Generative models for crystalline materials are typically trained to match the distribution of a structure database, while nothing in their training objective points them at specific design goals such as targeted properties. We use g...

Diffusion Quantum Monte Carlo Benchmark of Interlayer Binding and Charge Redistribution in Chemically Distinct Two-Dimensional Van Der Waals Bilayers

Interlayer interactions in two-dimensional materials can generate emergent phenomena absent in their constituent monolayers, including unconventional magnetic order, multiferroicity, and topological magnetic phases. Predicting such emergent behavior requires simultaneously resolving long-range dispersion, short-range o...

Toward Predictive Hydride Bond Energetics with Neural-Network Wavefunctions

Accurate prediction of transition-metal hydride (TM-H) bond dissociation energies (BDEs) remains challenging because of strong electron correlation, relativistic effects, and nuclear quantum contributions. Here, we assess the performance of neural-network variational Monte Carlo (NN-VMC) based on the Psiformer ansatz f...