Postdoctoral Fellow in Material Science & Mechanical Engineering
- The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned interatomic potential to incorporate materials response . Resulting models are implemented in LAMMPS and are used to perform ML-accelerated large-scale dynamics simulations to investigate the evolution of polarization and magnetization degrees of freedom. Efforts are also aimed at learning computationally lean and geometrically rich representations and designing methods for quantifying uncertainty of predictions. Qualifications: Familiarity with machine learning interatomic potentials, CPU and GPU parallelization, knowledge of LAMMPS and molecular dynamics, experience with first principles calculations of dielectric and magnetic response.
- Development of machine learning methods for exchange-correlation functionals. Current work in the group is focused on improvements and performance optimizations for the recently developed CIDER formalism for designing non-local XC functionals, with an eye toward applying the resulting functionals to currently intractable problems in catalysis and energy storage materials. Effort is aimed at generating training sets with high-order quantum calculations and designing combined models for the exchange and correlation energy using non-local features of electronic density and orbitals. Qualifications: Experience with developing efficient numerical algorithms and modifying electronic structure DFT or quantum chemistry software (e.g. Quantum Espresso, PySCF, GPAW), fluency with electronic structure theory and quantum chemistry, experience with machine learning regression methods.
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