Job Description

We are seeking a Computational Materials Scientist with expertise in either battery materials (solid/liquid electrolytes) or semi-conductors or catalysis. Elemynt is a computational materials research platform provider that works at the intersection of AI/ML, physics, and data-driven materials design. At Elemynt, the candidate is expected to be hands-on combining state-of-the-art ML interatomic potentials (MLIPs) with atomistic simulations to accelerate materials discovery.

Responsibilities

Apply MLIPs (MACE, M3GNet, NequIP, GAP) to predict properties of materials Run atomistic simulations using DFT codes (VASP, Quantum ESPRESSO, CASTEP, etc.) and MD packages (LAMMPS, GROMACS, etc.) Implement graph neural networks and diffusion models to generate and optimize electrolyte candidates Perform synthesis prediction and precursor selection, linking atomistic modeling to experimental feasibility Curate and query large-scale materials and reaction databases for training and validation Collaborate with experimental teams to validate predictions and feed results back into automated workflows

Requirements

PhD in Materials Science, Chemistry, Physics, or related field Demonstrated experience with MLIPs (MACE, M3GNet, NequIP, etc.) Proficiency with DFT codes (VASP, QE, CASTEP, etc.) and MD engines (LAMMPS, GROMACS, etc.) Experience with ASE, pymatgen or similar toolkits for job setup/automation Strong skills in Python and building scientific workflows Knowledge of synthesis prediction, precursor selection, or cheminformatics * Strong database and automation framework skills (Fireworks, Jobflow, Atomate, Airflow, Temporal)