Computational Materials Scientist

SES

Woburn, Massachusetts

Full Time

Posted March 17, 2026

Energy Storage

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Job Description

A Computational Materials Scientist role focused on performing atomistic simulations and quantum modeling to accelerate the discovery and development of advanced battery materials using AI-enhanced techniques in a collaborative, innovative environment.

Key Responsibilities

Conduct and oversee DFT, MD, and QM simulations of battery components such as electrolytes, coatings, and electrodes.
Develop and refine ML-enhanced force fields and surrogate models to accelerate simulation processes.
Generate high-quality structured simulation data for training AI property prediction models and material screening.
Automate complex simulation workflows to improve efficiency and scalability.
Collaborate with experimental teams to validate models and inform design iterations.

Requirements

Ph.D. in Mechanical Engineering, Materials Science, Chemical Engineering, or a closely related computational physics field.
Deep and extensive experience in atomistic simulation and quantum modeling, including proficiency with key QM DFT tools VASP, Quantum Espresso and MD simulations.
Strong background in electrochemical energy materials and extensive computational work focused on batteries and fuel cells.
Strong coding skills in Python along with related libraries like Pandas and TensorFlow for simulation workflow automation and data analysis.
Experience in developing or utilizing ML-enhanced force fields and surrogate models for materials prediction, or equivalent practical experience.
Ability to conduct and oversee DFT (Density Functional Theory), MD (Molecular Dynamics), and QM (Quantum Mechanics) simulations of battery components, including electrolytes, coatings, and electrodes.
Ability to develop and refine ML-enhanced force fields and surrogate models to accelerate simulation time scales and enable multi-scale simulation efforts.
Experience in generating high-quality, structured simulation data to serve as training sets for AI property prediction models and material screening modules.
Proficiency in automating complex simulation workflows using strong coding practices to enhance efficiency and scalability.
Ability to collaborate with experimental teams, leveraging a hybrid computational experimental literacy to validate models and drive design iteration.
Familiarity with advanced simulation tools such as VASP and Quantum Espresso, and data science libraries like TensorFlow and Pandas for managing and analyzing large datasets.