Computational Materials Scientist (Xora Portfolio Company)

Xora Innovation; Singapore; On-site; SGD 90,000 - 120,000; Full time

Job summary

A computational materials research platform provider in Singapore is seeking a Computational Materials Scientist. The role involves applying machine learning interatomic potentials and running atomistic simulations using DFT codes. Ideal candidates will have a PhD in a relevant field, experience with MLIPs, and strong skills in Python. You will collaborate with experimental teams to validate predictions and accelerate materials discovery in an innovative and fast-paced environment.

Qualifications

• PhD in relevant field required.
• Experience with MLIPs and DFT codes necessary.
• Strong background in Python for scientific workflows.

Responsibilities

• Apply MLIPs to predict material properties.
• Run atomistic simulations with various DFT codes.
• Implement graph neural networks for material optimization.

Skills

Expertise in battery materials or semi-conductors

Proficiency in machine learning interatomic potentials

Strong programming skills in Python

Synthesis prediction knowledge

Education

PhD in Materials Science, Chemistry, Physics, or related field

Tools

VASP

Quantum ESPRESSO

LAMMPS

GROMACS

pymatgen

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)