Computational Materials Scientist (Xora Portfolio Company)

Xora Innovation; Singapore; On-site; SGD 70,000 - 90,000; Full time

Job summary

A materials science research firm in Singapore is seeking a Computational Materials Scientist to advance research in battery materials and semi-conductors. The ideal candidate will apply machine learning techniques to atomistic simulations, have a PhD in a relevant field, and demonstrate proficiency in DFT codes. Experience with Python and scientific workflows is essential. This position offers an innovative environment focused on accelerating materials discovery.

Qualifications

• PhD in a relevant field, preferably with experience in Computational Materials Science.
• Proven experience with machine learning interatomic potentials and atomistic simulations.
• Strong programming skills in Python and experience with automation toolkits.

Responsibilities

• Apply MLIPs for predicting materials properties.
• Run atomistic simulations using specified DFT and MD codes.
• Implement models to optimize electrolyte candidates.

Skills

Machine Learning Interatomic Potentials (MLIPs)

Python

Database management

Education

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

Tools

VASP

Quantum ESPRESSO

LAMMPS

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)