Elemynt is a computational materials research platform provider working at the intersection of AI/ML, physics, and data‑driven materials design.
We are seeking a Computational Materials Scientist to combine state‑of‑the‑art machine learning 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).
Seniority level
Mid–Senior level
Employment type
Full‑time
Job function
Research and Science
Industries
Technology, Information and Media
Location
Singapore
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