Computational Materials Scientist

Responsibilities

• Conduct and oversee DFT (Density Functional Theory), MD (Molecular Dynamics), and QM (Quantum Mechanics) simulations of battery components, including electrolytes, coatings, and electrodes.
• Develop and refine ML-enhanced force fields and surrogate models to accelerate simulation time scales and enable multi-scale simulation efforts.
• Apply expertise in atomistic simulation and quantum modeling to solve key challenges in electrochemical energy materials (e.g., batteries/fuel cells).
• Generate high-quality, structured simulation data to serve as training sets for AI property prediction models and material screening modules.
• Contribute to the development of battery domain LLM features and advanced property-prediction models.
• Automate complex simulation workflows using strong coding practices to enhance efficiency and scalability.
• Collaborate with experimental teams, leveraging a hybrid computational + experimental literacy to validate models and drive design iteration.

• Education:
  
  Ph.D. in Mechanical Engineering, Materials Science, Chemical Engineering, or a closely related computational/physics field.
• Core Simulation Expertise:
  Deep and extensive experience in atomistic simulation and quantum modeling, including proficiency with key QM/DFT tools (VASP, Quantum Espresso) and MD simulations.
• Domain Focus:
  Strong background in electrochemical energy materials and extensive computational work focused on batteries/fuel cells.
• Coding Proficiency:
  Strong coding skills in Python (along with related libraries like Pandas and Tensor Flow) for simulation workflow automation and data analysis.
• ML Application:
  Experience in developing or utilizing ML-enhanced force fields and surrogate models for materials prediction., or equivalent practical experience.