Multiscale Modeling of Phase Evolution in Polymorphic Manganese Dioxide for Energy Applications
Zorikto Erdyneev
Department of Chemistry & Biochemistry
Faculty Supervisor: Nicole Adelstein
Manganese dioxide (MnO2) is a critical material for energy technologies such as catalysis, batteries, and supercapacitors. Our study seeks to deepen understanding of the kinetic mechanisms behind MnO2 phase transitions, a material that exhibits complex phase behavior due to its polymorphs thermodynamic proximity and structural flexibility. We employ atomic-scale quantum simulations, Machine Learning Force Fields (MLFF), and mesoscale phase-field modeling to predict phase transition pathways. Key objectives include: developing a mesoscale model using the MesoMicro code to simulate phase evolution under varying conditions; calculating kinetic barriers via solid-state Nudged Elastic Band (ssNEB) methods and MLFF-driven molecular dynamics to simulate nucleation, growth, and interfacial energies.