Machine Learning-Accelerated Electrolyte Modeling and Design

Duties

The electrolyte is an indispensable and critical component in any electrochemical storage device. The materials discovery of electrolytes for battery applications faces an enormous challenge where the design space is highly combinatorial in chemical, compositional, and topological spaces. To tackle this outstanding challenge, computational tools powered by machine learning techniques need to be developed to explore the corresponding design space efficiently. The focus of this project will be on (i) developing molecular representations used in generative AI and atomistic machine learning that can handle both liquid and polymer electrolyte systems and (ii) developing high-throughput and accurate molecular dynamics simulation techniques for fast access to transport coefficients. The candidate will be hosted by the TeC group (https://tec-group.github.io), which is part of the Ångström Advanced Battery Centre (ÅABC). This application-driven method development project will involve both national and international collaborations.

Requirements

The candidate must have:

• A Master's degree in theoretical chemistry (or related disciplines) and possess a good understanding of physical chemistry and materials chemistry or
• have completed at least 240 credits in higher education, with at least 60 credits at the Master’s level including an independent project worth at least 15 credits, or
• acquired essentially equivalent knowledge in some other way in Sweden or abroad.
• Excellent capabilities in written and oral English.
• Programming skills with Python.

Consideration will also be given to good collaborative skills, drive and independence, and how the applicant’s experience and skills complement and strengthen ongoing research within the department, and how they stand to contribute to its future development.

Additional qualifications

• Previous experiences with molecular dynamics simulations and (periodic) quantum chemical calculations.
• Previous experience with machine learning applications in chemistry, including experience with at least three of the following Python libraries: Pandas, scikit-learn, PyTorch, TensorFlow, JAX, RDKit.
• Previous experiences with collaborative coding with GitHub.

About the employment

The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100%. Starting date 2025-09-01 or as agreed. Placement: Uppsala.

Application link and deadline

Please submit your application by 24 April 2025, UFV-PA 2025/888.

Application link