Computational Chemistry Intern
PhysicsX
Shoreditch, London, UK
Posted on Apr 30, 2025
PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software.
We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility.
The role: As a Computational Chemistry Intern, you will join us over Summer 2025 and contribute to cutting-edge research at the intersection of materials science, machine learning, and computational chemistry. You will be responsible for establishing the state-of-the-art in publicly available datasets and ML models that interface with Density Functional Theory (DFT) for catalyst discovery. Your work will culminate in applying the most promising model to screen for non-rare earth oxygen evolution reaction (OER) catalysts, helping to accelerate the path toward sustainable energy materials.
This exciting internship offers a unique opportunity to explore data-driven approaches for materials discovery and gain hands-on experience with tools used in AI for science. You’ll be exposed to both open-source materials databases and modern machine learning workflows, contributing to our growing capabilities in autonomous experimentation and high-throughput screening.
Join us in this dynamic role, where your curiosity, research mindset, and coding skills will help push the boundaries of materials innovation!
What you will do
- Review and benchmark publicly available datasets and machine learning models designed for DFT-based catalyst screening.
- Evaluate the suitability of models for various catalyst types (e.g., transition metal oxides, alloys).
- Select and apply the most promising model to identify potential non-rare earth OER catalysts.
- Prepare a comparative analysis of dataset quality, model performance, and screening outcomes.
- Document the workflow in reproducible code (e.g., Jupyter notebooks or scripts) with clear instructions for future use.
- Contribute to internal knowledge sharing via presentations or technical reports.
What you bring to the table
- Currently pursuing a Master’s or PhD degree in Chemistry, Materials Science, Physics, Chemical Engineering, or a related field.
- Familiarity with DFT and its role in materials design.
- Strong programming skills in Python; experience with libraries such as scikit-learn, PyTorch, or ASE is a plus.
- Interest in machine learning applied to physical sciences and materials informatics.
- Experience with materials databases (e.g., Materials Project, OQMD, Open Catalyst Project) is desirable.
- Excellent problem-solving and analytical thinking skills.
- Strong written and verbal communication skills.
- Ability to work independently and collaborate effectively with technical teams.
This is a paid internship opportunity from June 2025 - August 2025.
We believe diversity fuels innovation, and we're building a culture where everyone belongs. We're proud to be an equal opportunity employer, welcoming talent of all backgrounds, identities, and experiences. Changing the face of tech takes action, which is why we actively encourage individuals from historically underrepresented groups to apply.