Industrial partnership

Computational chemistry, anchored in the fundamental laws of quantum physics, provides a microscope into the quantum world of atoms and molecules that make up the fundamental building blocks of the world around us. With this microscope, we get a front-row seat view of everything from material properties to chemical transformations to catalytic activity, which underlies the performance of many of today’s key technologies.

AI-based methods powered by GPUs are advancing us from targeting the idealized proof of principle to the messy real-world systems faced in an industrial setting. The key technological development behind these opportunities is machine learning potentials, which, instead of solving the Schrödinger equation in hours, predict the solution in milliseconds, enabling us to study much larger systems on long-time scales. While powerful, their deployment and training are complex and require expert knowledge.

Over the last couple of years, the Bore Research Group has developed an active learning framework for training machine learning potentials for any molecular system. Our computational frameworks provide a fully automated workflow, combining state-of-the-art electronic structure programs with equivariant machine learning potentials, interfacing them with the cloud or supercomputers, all while sitting on your desktop.

We are now ready to apply these tools to real-world systems and are interested in developing research collaboration with the private sector, especially within the following topics:
- Battery materials
- Fuel cells
- Mechanistic studies and optimization of industrial chemical processes
- CO\(_2\) capture and conversion
- Hydrogen hydrolysis
- Ice friction of materials and ski wax
We of course welcome suggestions from potential partners.

Joint funding opportunities

Because we are in these early stages of establishing collaborations with partners, we are prepared and committed to being a driving force for the successful initiation and completion of such a research collaboration. In this regard, we have identified the following upcoming funding opportunities:
- Project partner for innovation project in Environmental Friendly energy [Deadline 16 October 2024]

The following have funding opportunties have passed their deadline, but may reopen for additional rounds:
- Project partner for industrial PhD with AI [Deadline 5 June 2024]