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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