About Workshop

Participant Feedback

This workshop brought together a group of people that otherwise would not meet, which was really valuable for me. I learned a great deal about atomistic simulations and gained a much deeper understanding of molecular dynamics.

I learnt a lot from the workshop talks and conversations during breakfast and coffee time, such as generic form of a dynamical system, numerical deflation and surface hoping models. I think this workshop offered me many new knowledge and potential collaborations. Plus, the staff are really awesome!

The workshop effectively brought together researchers across a wide range of disciplines in applied mathematics and computational materials science. The workshop format was excellent for building stronger connections with other attendees compared to other conference events I’ve attended.

The goal of this workshop was to bring together researchers in computational materials science and mathematics to discuss mathematical models and algorithms essential for understanding and simulating materials at the atomic, molecular, and particle scales.

Set against the backdrop of emerging high-performance computational resources, such as the UK’s forthcoming first exascale supercomputer to be sited in Edinburgh, the workshop focused on the development of new mathematical and algorithmic methodologies which aimed to make the most of the computational power of future computer architectures in materials simulation. The workshop stimulated research collaborations between researchers from the UK, EU and USA, and offered the opportunity for mathematicians to learn more about the new challenges and opportunities these computational platforms could bring.

Topics discussed included novel approaches to computational multi-tasking and sampling; optimisation methodologies such as numerical continuation and deflation; coarse-graining and model reduction; and the rigorous mathematical analysis of such methodologies.

The workshop was partially supported by the Edinburgh Mathematical Society, Glasgow Mathematical Journal Trust and Research England under the Expanding Excellence in England (E3) funding stream, which was awarded to MARS: Mathematics for AI in Real-world Systems in the School of Mathematical Sciences at Lancaster University.