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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.

• 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

• 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)