Computational Materials Scientist

ELEMYNT PTE. LTD.; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A materials science company in Singapore is looking for a PhD-qualified professional to apply MLIPs for predicting material properties, run simulations, and collaborate with teams on experimental validations. Ideal candidates should have experience with DFT codes and strong programming skills in Python, along with a robust understanding of synthesis prediction and database management.

Qualifications

• PhD in Materials Science, Chemistry, Physics, or related field.
• Demonstrated experience with MLIPs (MACE, M3GNet, NequIP).
• Proficiency with DFT codes and MD engines.
• Strong skills in Python and building scientific workflows.
• Knowledge of synthesis prediction or cheminformatics.

Responsibilities

• Apply MLIPs to predict properties of materials.
• Run simulations using DFT codes and MD packages.
• Implement graph neural networks for candidate optimization.
• Collaborate with teams to validate predictions.

Skills

Machine Learning Interatomic Potentials (MLIPs)

Python

Database skills

Framework automation (Fireworks, Jobflow, etc.)

Education

PhD in Materials Science

Tools

VASP

Quantum ESPRESSO

CASTEP

LAMMPS

GROMACS

ASE

pymatgen

Job description

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)