Project Ideas

We are currently exploring new development projects to add to the applications already available in Compular Lab. Help shape our development direction! Let us know if any of the ideas below are of interest to you, and those will be the projects that we proceed with. We are primarily targeting companies in the battery industry, but if you are in a different field and find the project ideas relevant for your needs, please let us know.

 

Idea 1

Predict electrochemical stability

We can help you estimate the electrochemical stability window, either with redox potentials or with HOMO–LUMO levels.

We predict redox potentials using density functional theory (DFT) by calculating the change in free energy upon addition or removal of an electron to a molecule or an ion using a Born–Haber thermodynamic cycle. In such calculations, the component of interest is put in an implicit solvent that mimics the permittivity of the surrounding solvent. For reduction potentials, it is also standard practice to explicitly take into account the influence of nearby cations. Calculations of these kinds are well-established in the literature and produce results that are in good agreement with experiment.

We compute HOMO–LUMO levels with DFT in the condensed phase and project electronic states onto individual molecules. Using our patented trajectory analysis method, we can also analyse the electronic energy levels with respect to dynamic species such as solvation shells and adsorbed species in the electrochemical double layer (EDL). This information reveals which components of the electrolyte are likely to be vulnerable to oxidation (high-energy HOMO state) or reduction (low-energy LUMO state) taking into account the condensed matter environment.

These two methods, redox potentials and HOMO–LUMO levels, strike different trade-offs, with the first enabling higher order density functionals to be used, while the second, although limited to simple functionals, has better scalability and incorporates the condensed matter environment more directly. We have automated the methods to do them at scale, allowing you to screen a large number of compositions.

Idea 2

Understand SEI/CEI formation

We are developing a streamlined workflow tailored for detailed studies of solid electrolyte interphase (SEI) and cathode electrolyte interphase (CEI) formation on battery electrodes. This workflow offers a robust, multiscale approach for examining the underlying reactions and mechanisms essential to battery performance and stability.

  1. Reactive molecular dynamics (MD): Simulate initial chemical interactions at the electrode surface to capture bond formation and breakdown processes efficiently. This step reveals primary reaction pathways and intermediate products in SEI/CEI formation.
  2. Nonreactive polarisable MD: Utilising our patented trajectory analysis method, we characterise the concentrations and transport properties of dynamic structures such as solvation shells in the bulk and along the electrochemical double layer (EDL).
  3. Density functional theory (DFT): DFT is applied to key steps on a case by case basis, identification of rate limiting steps to derive energy barriers, verify reaction energetics, stability, and electronic properties of SEI components.
  4. Kinetic Monte Carlo (KMC): Building on reaction pathways and free energy profiles from reactive MD, and dynamic structures and transport properties from nonreactive MD, KMC enables longer time-scale simulations to track the growth and evolution of SEI/CEI layers, helping to understand its composition and dynamics as it forms over time.

By combining these methods, our workflow equips battery engineers with a comprehensive toolset to deepen understanding of SEI formation enabling improved battery materials and interfaces.

Idea 3

Characterise polymer chemistries

We use molecular dynamics simulations to explore and optimise polymer chemistries—such as ethers, carbonates, ionomers, and cross-linked networks—alongside different lithium salts and additives to enhance battery performance.

Our automated simulation tool allows you to build polymer electrolytes from the ground up, predicting key properties such as:

  • Transport properties: Ionic conductivity, diffusion coefficients, transference numbers, and thermodynamic factors across various temperatures and concentrations.
  • Glass transition temperature (Tg): Understanding how Tg and structural properties correlate with transport performance.
  • Mechanical properties: Stress-strain behaviour, elastic modulus, tensile strength, and elongation at break to assess mechanical strength.

This tool will help you design polymer electrolytes as well as to optimise and understand the material prior to doing experimental work.

Idea 4

Explore the thermodynamics of active materials

We are developing a workflow for analysing the thermodynamics of active materials. Using density functional theory and cluster expansions, we construct models that provide detailed insights into the thermodynamics of multi-component, crystalline materials. In particular, this includes the thermodynamics of lithiation in these materials. Using such models, we:

  • Predict open circuit potential
  • Identify stable phases and tie them to the underlying atomic structure
  • Study the impact of temperature with Monte Carlo simulations

This unlocks a path to optimising and understanding active materials without any lab work.

 

Interested?

If one of the project ideas resonates with you, please reach out by email at and let us know which one aligns with your goals.

Didn’t find what you’re looking for?

If none of the topics align with  your current interests and needs, we are always open to exploring new project ideas and tackling unique challenges. Book a meeting or email us at to discuss your specific needs.