Computational Chemist - ReaxFF

SES AI Corp. (

NYSE:

SES) is dedicated to accelerating the world's energy transition through groundbreaking material discovery and advanced battery management. We are at the forefront of revolutionizing battery creation, pioneering the integration of cutting-edge machine learning into our research and development. Our AI-enhanced, high-energy-density and high-power-density Li-Metal and Li-ion batteries are unique; they are the first in the world to utilize electrolyte materials discovered by AI. This powerful combination of "AI for science" and material engineering enables batteries that can be used across various applications, including transportation (land and air), energy storage, robotics, and drones . To learn more about us, please visit: www.ses.ai

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 are used to discover and deploy AI-enhanced battery solutions. What we

Need:

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.

Essential Duties and Responsibilities:

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 and/or

Experience:

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.

Preferred Qualifications:

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 like 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.