Computational Chemist – ReaxFF

What We Offer

• A highly competitive salary and robust benefits package, including comprehensive health coverage and an attractive equity/stock options program within our NYSE-listed company.
• The opportunity to contribute directly to a meaningful scientific project—accelerating the global energy transition—with a clear and broad public impact.
• Work in a dynamic, collaborative, and innovative environment at the intersection of AI and material science, driving the next generation of battery technology.
• Significant opportunities for professional growth and career development as you work alongside leading experts in AI, R&D, and engineering.
• Access to state-of-the-art facilities and proprietary technologies used to discover and deploy AI-enhanced battery solutions.

The SES AI Prometheus team is seeking an exceptional Computational Materials & Molecular‑Modeling Scientist to specialize in physics‑based simulation, utilizing reactive force‑field development and MD/DFT coupling. This role is critical for generating validated atomistic and mesoscale data that directly feeds our AI‑driven materials discovery workflows. As a key scientist, you will bridge physics‑based simulation with advanced AI methods, driving materials discovery through computational rigor.

• Advanced Simulation & Modeling
  • Develop and apply advanced simulation techniques, specializing in Reactive Force Fields (ReaxFF) and MD/DFT coupling, to model complex materials phenomena.
  • Conduct and analyze multi‑scale computation projects, covering atomistic simulation up to mesoscale modeling.
  • Utilize and maintain advanced simulation tools, including VASP, LAMMPS, GROMACS, and CP2K, for high‑fidelity simulations.
  • Perform rigorous physics‑based validation on all simulation outputs to ensure accuracy and reliability.
• AI Data & Workflow Development
  • Generate and validate high‑quality atomistic and mesoscale datasets used for training ML potential and other scientific AI models.
  • Develop advanced simulation workflows and contribute to the creation of ML potentials and AI‑driven materials discovery tools.
• Coding & Collaboration
  • Apply strong coding skills (e.g., Python, C++, FORTRAN) to automate complex simulation workflows and enhance computational efficiency.
  • Maintain a solid understanding of battery chemistry modeling and collaborate effectively across computational and experimental teams.

• Education:
  
  Ph.D. in Computational Chemistry of Energy Materials, Materials Science, or Computational Physics.
• Core Simulation Expertise:
  Deep, demonstrated expertise in advanced physics‑based simulation, including proficiency with DFT, MD, and Quantum Mechanics (QM) simulation tools.
• Reactive Potentials:
  Proven experience with Reactive Force Fields (ReaxFF) and methods for MD/DFT coupling.
• Coding & Tools:
  Strong coding ability in languages such as Python, C++, or FORTRAN, and deep practical experience with simulation codes like VASP, LAMMPS, GROMACS, or CP2K.
• Domain Focus:
  Solid understanding of battery chemistry modeling and experience in generating and validating atomistic and mesoscale data.

• Machine Learning Integration:
  Experience with Neural Networks and Deep Learning, particularly in applying them to develop ML potential. Familiarity with geometric deep learning libraries like PyTorch Geometric is a plus.
• Multiscale Methods:
  Experience with specialized methods such as the Materials‑Point Method.
• Hybrid Literacy:
  Experience with experimental literacy, understanding characterization methods such as SEM, TEM, XPS, Raman, EIS, NMR, DSC, or TGA.
• Background:
  Previous experience at advanced simulation labs, battery multiscale modeling groups, or in the computational chemistry of energy materials.

Mid‑Senior level

Full‑time

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