Berzelius Research Showcase

Below is a snapshot of a few of the groundbreaking research projects currently taking advantage of Berzelius high performance computing resources.

Training AI Models with Synthetic Images

Artificial Intelligence (AI) can help oncologists efficiently and precisely identify areas requiring radiation therapy for cancerous tumors. However, the scarcity of medical data for training AI models remains a challenge. To address this, researchers are turning to synthetic medical images. Leveraging the Berzelius supercomputer, this method has accelerated progress, avoiding years of potential waiting.

Anders Eklund’s research team at Linköping University utilizes the Berzelius supercomputer at the National Supercomputer Center (NSC) to generate and evaluate synthetic MRI images of brain tumors. Conducting this research on a standard powerful computer would have required more than five years. To evaluate these synthetic images, the team uses AI to compare them with actual MRI scans of brain tumors, employing models trained on both image types.

Perception for Autonomous Vehicles

The research has two branches, one is situation awareness of autonomous trucks and busses working closely with Scania; the other is sonar perception and navigation for autonomous underwater vehicles as part of the Swedish Maritime Robotics Centre, SMaRC.

The team is trying to achieve increased autonomy through the use of the latest neural network methods. The researchers have benefited greatly from the Berzelius GPU resources which have reduced the training times by s orders of magnitude. SMaRC has now been made a permanent Centre. The stakeholders have specifically asked the team to continue the sonar modeling using neural nets.

Machine Learning assisted Quantum Computing

The prospect of building computers based on the principles of quantum mechanics, using quantum bits (qubits), has sparked a massive research effort involving both academic institutions and private enterprises. In Sweden, the Wallenberg Centre for Quantum Technology (WACQT) leads this effort.

In contrast to bits in a classical computer, qubits are highly susceptible to noise, which at present severely limits their practical use. Quantum error correction (QEC), distributing the protected information in an entangled logical qubit state over many physical qubits, will therefore be a necessary component of future quantum computers. QEC requires a decoder, a classical algorithm that interprets a set of measurements to predict the most likely set of errors. The group of Mats Granath at the University of Gothenburg works on using deep learning, such as graph neural networks, to train decoders using both simulated and real experimental data. For this continued effort the resources at Berzelius are instrumental, allowing for both training the deep networks and generating the massive amounts of simulated data required.

Identifying Key Elements in Self-Supervised Representation Learning

Representation learning is a process in machine learning where algorithms extract meaningful patterns from raw data to form representations that contain higher-level semantic concepts like objects. This approach is crucial for tasks such as classification, retrieval, and clustering, enabling deeper insights into complex data.

The research team’s focus is to develop a better understanding of current representation learning methods, particularly self-supervised learning techniques in the visual domain. They aim to analyze the effects of various inductive biases, such as object-centric bias or other architectural choices, and leverage these insights to develop better and more generalizable representation learning approaches.

The Berzelius supercomputer at the National Supercomputer Center (NSC) provides the computational power necessary for the large-scale experiments. It enables the researchers to efficiently train and compare object-centric models with large pre-trained vision foundation models. The cluster’s reliability and excellent user support have ensured substantial and seamless progress throughout the project.

Peptide Binders designed by Artificial Intelligence

Patrick Bryant’s research team applies Artificial Intelligence (AI) to protein structure prediction and interaction analysis. They are developing neural networks to design specific linear and cyclic peptide binders, including those that can bind two target proteins simultaneously.

Leveraging Berzelius’s computing power, the team has developed EvoBind2 for rapid peptide design targeting diverse receptors in diabetes and cancer research. They are also working on EvoBind-multimer for dual-protein targeting, potentially enabling targeted protein degradation. Overall, they are not just predicting protein structures and interactions; they are designing the future of targeted therapies.

The team plans to design peptide binders for 400 human cancer cell surface receptors, aiming to create a range of peptides for various cancer types. This work lays the foundation for personalized diagnostics and targeted therapies, pushing the boundaries of AI-driven drug design. Qiuzhen Li is leading this initiative.