Computing for Sustainable Innovation: 3rd Exascale and Scalable AI Workshop Agenda
Day 1 – 16 June 2025
The STFC AI Strategy, Adriano Agnello STFC Hartree Centre and Valerie Farr STFC
The STFC vision is the creation of responsible AI knowledge and solutions at scale and pace, to maximize the value of data across the research and innovation system for growth and prosperity. This presentation will illustrate the STFC AI ecosystem, the STFC AI Strategy core strategic objectives, and key enablers.
This session will offer insights from leading UK research facilities on how national infrastructure is enabling innovation through cutting-edge compute and AI. Attendees will hear how access to these capabilities can accelerate R&D pipelines, open new markets and support strategic decision-making.
Building Digital Twins of the Universe with the DiRAC HPC Facility, Mark Wilkinson DiRAC
Abstract:
Cosmological simulations are essential tools for the interpretation of observations of the Universe and its constituents. In essence, these simulations constitute digital twins, providing the means to explore different scenarios for the evolution of the Universe which can be compared with actual data. I will present examples of state-of-the-art simulations which have been carried out by researchers using the DiRAC HPC Facility. I will also present on-going work to develop AI-based surrogate models to enhance the inclusion of “sub-grid” physics in the next generations of cosmological simulations. Finally, I will discuss the benefits of a co-design, workflow-centred approach to the deployment of large-scale computing systems for both simulations and AI.
Bio:
Mark Wilkinson is the national director of the STFC DiRAC HPC Facility (www.dirac.ac.uk), which provides high performance computing resources for the theoretical astrophysics, particle physics, cosmology and nuclear physics communities in the UK. He is a Professor of Astrophysics at the University of Leicester, specialising in the study of dark matter in galaxies using a combination of observations, theoretical models and machine learning. His recent work focusses on the use of machine learning and AI to enhance and accelerate simulations in astrophysics and cosmology. He has published almost 100 peer-reviewed papers and has almost 20,000 citations.
Mark was the editor of the 2019 community-led white paper “UKRI National Supercomputing Roadmap 2019-30” and chaired the editorial board for the peer-reviewed “UKRI Science case for UK Supercomputing” which was published in 2020. He recently chaired the STFC AI Strategy Development Working Group. He currently co-Chairs the STFC Exascale Working Group and is a member of the UKRI Advisory Group for Digital Research Infrastructure (AGD).
Isambard-AI: a template for sustainable, leadership-class AI supercomputers, Simon McIntosh-Smith University of Bristol
Federating National Compute and Data, Jon Hays UKRI
Federating AI resource, Paul Calleja University of Cambridge
Keynote on USA’s Department of Energy AI programme, Rick Stevens
Abstract:
In this talk, I outline the concept and strategic importance of an AI Factory for Science. This is a specialized platform designed to industrialize and scale AI model development, deployment, and maintenance to accelerate scientific discovery. I highlight the need for large-scale, optimized AI infrastructure tailored to advanced scientific workloads, emphasizing requirements such as massive GPU resources, integration with high-performance computing, and domain-specific models. I also discuss key applications across scientific fields, including energy, physics, medicine, and space, supported by partnerships and advanced hardware. Finally, I address the transition plan, cost considerations between on-premises and cloud deployments, and the evolving role of AI-enhanced supercomputing in driving future breakthroughs.
Bio:
Part quantum theorist, part AI strategist, and part national laboratory futurist, Rick Stevens is a Professor of Computer Science at the University of Chicago and the Associate Laboratory Director of the Computing, Environment and Life Sciences (CELS) Directorate and Argonne Distinguished Fellow at Argonne National Laboratory. His research spans the computational and computer sciences from high-performance computing architecture to the development of tools and methods for bioinformatics, cancer, infectious disease, and other challenges in science and engineering — all in the service of unlocking the next leap in scientific discovery. Moving effortlessly between biology, materials science, supercomputing, and macroeconomics, he spends his weekends camping under the stars… or calculating optimal shielding for a fusion starship.
Explore the tangible impact of large-scale computing and AI across sectors. This session will highlight real-world case studies where scalable digital technologies have improved productivity, operational efficiency and innovation for both businesses and public service delivery.
UK AI Opportunities plan, DSIT
A Trinity of Scale: Exascale Simulation, AI, and the Fusion Moonshot, Rob Akers UKAEA
At UKAEA, we are tackling a grand challenge: to deliver net power safely and affordably to the grid from magnetically confined fusion — a tightly coupled system-of-systems problem where traditional “build, test, learn” approaches are too slow, costly, and risky. There is neither time, money, nor political appetite to learn from the “rapid unscheduled disassembly” of a tokamak. Instead, we must expose emergent phenomena and pre-empt Black Swan failure modes through the transformative confluence of high-performance computing and AI at the exascale. Our approach is to design in silico, intelligently combining high-fidelity simulations (predominantly open tools) with AI methods, surrogate models, and lower-order solvers. This talk will explore how scalable compute and AI are helping reduce cost and tame complexity in fusion powerplant design — and preview how UKAEA’s fusion “Moonshot” is positioned at the heart of a second giant leap: the UK’s first AI Growth Zone, designed to ensure the UK is an AI maker, not an AI taker.
Discovery of high entropy ceramics for extreme environments, Richard White Lucideon
Lucideon is developing novel ceramic based materials for extreme environments, in particular extreme temperature, high entropy ceramics, where the number of potential compositions far exceeds an experimental programme of fabrication and evaluation.
The Need of Compute and AI to tackle the NHS’s Increased Demand, Janet King NHS England NW Digital Transformations Director
Focusing on the future of mobility, this session will demonstrate how advanced simulation and AI tools are being used to optimise battery performance, accelerate design cycles and maintain a competitive advantage in the fast-evolving electric vehicle landscape.
Accelerating Battery Development with high-fidelity microstructure and particle scale models: through manufacturing to performance, Francois Usseglio-Viretta NREL
Linking atomistic simulations on supercomputers to larger scales for designing better batteries, Chris Skylaris University of Southampton and Faraday Institution
Software tools for the next-generation digital twin of batteries for exascale machines, Karthik Chockalingam STFC Hartree Centre
Improving Battery Safety with AI-Powered Battery Management, Brain Smith Eatron Technologies
The government recently shared that ‘successful deployment of fusion energy would be globally transformative and allow the UK to export the technology to a global fusion market expected to be worth trillions of pounds in the future.’ In this session, we will discuss how cutting-edge technologies are essential to building comprehensive alternative energy sources.
Generative AI for Inertial Fusion Energy Science, Vadim Elisseev IBM
Abstract:
Fusion energy research has long captured the public imagination for its applications to fundamental physics, material sciences, and as a clean, low-carbon source of electricity. Recent breakthroughs at the National Ignition Facility (NIF), where lasers were used to initiate nuclear fusion in hydrogen isotopes, mark significant progress toward realizing inertial fusion energy (IFE). However, achieving sustainable IFE remains a formidable challenge due to the vast and complex parameter space involved in optimizing conditions for thermonuclear ignition. To address this complexity, we investigate the application of generative artificial intelligence (Generative AI) methodologies, focusing on how the integration of large language models (LLMs), and deep reinforcement learning (DRL) can accelerate the autonomous discovery of optimal IFE configurations. We present our ongoing research into how these AI techniques can enable more efficient exploration, control, and optimization of fusion experiments, potentially transforming the path toward viable fusion energy.
Fusion Innovation at Exascale: The need for high-fidelity modelling for real-world prototypes, Gurdeep Singh Kamal Tokamak Energy
Tokamak Energy is pioneering the development of fusion power through advanced spherical tokamaks and high-temperature superconducting (HTS) magnet technologies. As we push the boundaries of plasma performance and magnet design, the demand for high-resolution, multi-physics modelling and simulation grows exponentially. This talk will explore how Machine Learning and Exascale Computing can unlock new frontiers in fusion innovation. We will present our current modelling capabilities, focusing on integrated high-fidelity simulations. model driven experimental design and whole system modelling needs. By utilising exascale computing, we aim to take advantage of the enhanced predictive power and speed to allow for faster iteration, deeper insight, and ultimately, a shorter path to commercial fusion.
Digital Engineering for Fusion Energy, Andrew Davis UKAEA
Fusion energy systems present some of the most demanding engineering challenges of our time, requiring robust performance under extreme thermal, nuclear, mechanical, and electromagnetic loading. As the design of pilot-scale fusion devices accelerates, multiphysics driven simulation is playing a central role in enabling predictive, integrated, and agile engineering workflows. This talk explores how digital engineering—anchored by high-fidelity simulations, advanced models, and multi-scale, multi-physics coupling—is transforming the design and qualification of fusion structural components. Drawing on examples from national and international fusion programs, we demonstrate how digital workflows are helping to reduce design iteration cycles, improve system reliability, and bridge gaps between simulation and experiment. The presentation will also highlight open research questions and the role of cross-disciplinary collaboration in advancing the digital infrastructure needed to support timely delivery of pilot-scale fusion reactors.
Artificial Intelligence to accelerate development of sustainable energy solutions, Jonathan Booth Hartree Centre
In the development of solutions for sustainable energy, AI can accelerate or bypass expensive simulations, and aid the training of advanced control and optimisation agents. Here, we show a selection of examples mostly focused on fusion energy, and perspectives and requirements for scalable computing paradigms.
Day 2 – 17 June 2025
AI for Large-Scale Experimental Facilities, Jeyan Thiyagalingam STFC
Sustainability Challenges for Future Computing, Jim Sexton IBM
As computing plays an increasingly significant role in an organisation’s environmental footprint, adopting green computing practices can meaningfully reduce IT-related emissions. This session will explore how sustainable computing strategies can support organisations in reaching their wider carbon reduction targets, highlighting practical steps and technologies that can drive greener, more energy-efficient digital operations.
Leveraging custom hardware with LBANN, Brian van Essen LLNL
AI-assisted DevOps for HPC, David Beckingsale LLNL
Overview of Hartree Centre projects on sustainability, Sarah Hanrahan STFC Hartree Centre
TBC, David McDonagh STFC Scientific Computing
Material Science in 21st Century has high impact on sustainability and efficiency of products. Through innovation in ecology friendly materials and processes it drives economic growth and reduces the environmental impact of industry.
Optimizing end-to-end time to solution with OpenFold on El Capitan, Nikoli Dryden LLNL
Autonomous Multiscale: Driving Discovery with Smart Simulations, Peer-Timo Bremer LLNL
Multi-scale modelling – An Industrial Perspective, Misbah Sarwar and Johnson Matthey
Multi-scale simulations, ranging from electronic structure to continuum-based approaches have become embedded in product development cycles, innovating the way in which new products are developed. The talk will give an overview of how multi-scale modelling combined with advanced characterization techniques are being used in industry to understand the structure and activity of catalytic materials that are used to accelerate the transition to net zero. The talk will also discuss how newly developed approaches such as MLIPs might fit into such a workflow and be used to accelerate the catalyst discovery process.
AI and Quantum Computing for Microbiome Data, Ruediger Zillmer Unilever
Agentic Frameworks for autonomous discoveries, Michail Smyrnakis STFC Hartree Centre
This presentation explores agentic frameworks—AI systems capable of autonomous decision- applied across diverse scientific and simulation domains. We highlight how large language models and multi-agent systems can accelerating discovery in materials science and drug development, while enabling strategic reasoning in game environments and adaptive modelling for pandemics.
Discover how organisations are preparing for next-generation digital demands. This session will showcase scalable AI and exascale-ready solutions that can support long-term growth, resilience and agility across a wide range of industries.
Pannel Discussion, moderator Vassil Alexandrov
Panel members: Mark Wilkinson, Brian van Essen (LLNL), Dave Brains (IBM), Timo Bremer (LLNL), Rob Akers (UKAEA).
Roadmapping – way forward discussion, moderator Erik Draeger
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