ML Potentials For Molecular Dynamics Intern

Key Skills

Algorithms, AWS, Azure, Computer Vision (CV), Machine learning techniques, Python Programming

Job Description

At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team in Human-Centered AI, Human Interactive Driving, Energy and Materials, Machine Learning, and Robotics.

This is a Summer 2024 paid 12-week internship opportunity. Please note that this internship will be a hybrid in-office role.

The Team

The Energy and Materials Division at TRI is building tools to accelerate the design and discovery of new materials, fostering a transition to more sustainable mobility. Our research applies AI, data-driven methods, and automation to materials science, and spans the atomic to the device scales. Our projects often involve collaboration with scientists from universities and national labs. Interns will be involved in industrial research on topics of broader interest to the general materials science community, and several previous intern projects have resulted in peer-reviewed publications in journals such as npj Computational Materials and Chemical Science.

The Internship

We aim to apply interatomic potentials to investigate kinetics phenomena such as nanoparticle reconstruction, lithium diffusion, and phase transitions in solid-state materials. The project focuses on harnessing the predictive power of these machine learning-based potentials to accurately simulate reconstruction as a function of composition or under the influence of external reactants, enhancing our understanding of their structural dynamics and properties. This approach is expected to advance our insights into the behavior of materials and interfaces under various conditions and contribute to the development of more efficient energy storage materials and nanotechnology applications. The project may also involve collaboration with experimental groups to validate the computational models.

Qualifications

• Applicant must be pursuing a doctorate in materials science, chemical engineering, mechanical engineering, physics, applied mathematics, computer science, other engineering, or related field
• Fluent in Python

Bonus Qualifications

• Experience with using or developing ML interatomic potentials
• Experience with Density Functional Theory
• Experience with molecular dynamics

Please add a link to Google Scholar and include a full list of publications when submitting your CV to this position.

The pay range for this position at commencement of employment is expected to be between $45 and $65/hour for California-based roles; however, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. Note that TRI offers a generous benefits package including vacation and sick time. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

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