HAMLET-PHYSICS 2026 Conference

Session

Plenary: Molecules, matrials, structure

19 Aug 2026, 14:45

Margrethe Bohr Salen (Niels Bohr Building)

20. From AI-Assisted Characterization to Autonomous Discovery of Novel Ferroelectric Materials

Prof. YAPING QI (Tohoku University)

19/08/2026, 14:45

Plenary

Recent advances in large language models (LLMs), foundation models, and AI agents are transforming materials science from AI-assisted characterization toward autonomous scientific discovery. While machine learning has achieved remarkable success in materials property prediction, discovering entirely new functional materials remains challenging because of the enormous chemical design space and...

23. Exploring hidden structure in water using polarizable atom interaction neural networks

Dr Fredrik Grote (Department of physics, Stockholm University)

19/08/2026, 15:05

Plenary

Water is a key-ingredient for life as we know it and plays a central role in maintaining Earth’s climate within the limits that life can tolerate. Water is all around us, yet its properties are radically different from essentially all other substances. The density maximum at 4 degrees, that makes ice cubes float in our drinking glasses, is the most famous example among water’s anomalous...

14. Predicting Magnetic Interactions from Time-Series Magnetization Data Using Explainable Machine Learning

Maryna Pankratova (Uppsala University)

19/08/2026, 15:25

Plenary

Machine learning methods have proven useful for addressing a wide range of tasks in science and technology. Recent applications in materials science include materials discovery, predicting material properties, such as the Curie temperature, constructing phase diagrams, accelerating first-principles calculations, and more. One of the challenges in magnetism theory is accurately determining the...

35. Predicting Superconductivity using Machine Learning

Ms Amalie Falkenberg Davidsen, Ms Frida Birkedal Nielsen, Mr Hugo Schreckenberg (University of Copenhagen), Rasmus Madsen (University of Copenhagen), Silas Billeschou Schack (University Of Copenhagen)

19/08/2026, 15:45

Poster

Room temperature Superconductors are one of the most exciting materials in condensed matter physics, but the hunt for such a material still remains ongoing. The physics of known high temperature superconductors are not well understood and the mechanisms vary and depends on chemical composition and crystal structure. To circumvent the problem of understanding the physics, we instead make use of...

5. High-Throughput Discovery of Magnetic Materials:By Machine Learning and its combination with First-Principles Methods

Atefe Nayamadi Mahmoodabadi (Uppsala)

19/08/2026, 15:50

Poster

High-Throughput Discovery of Magnetic 2D Materials:
From Physics-Informed Machine Learning to Correlated Electron Systems

The discovery of magnetic two-dimensional (2D) materials has created new opportunities for spintronics, quantum technologies, and low-dimensional correlated electron physics. However, the enormous chemical and structural search space of magnetic monolayers makes...

21. Gaussian sum smoothing for continuously monitored quantum systems

Julien Pinske (University of Rostock)

19/08/2026, 15:55

Poster

The wave function encodes our knowledge of a quantum system prior to a measurement. By incorporating posterior information, one can retrospectively improve the estimate of unknown outcomes of earlier measurements. For a continuously monitored system, this inference problem is described by a Hidden Markov model whose forward and backward dynamics are governed by stochastic master equations. As...

30. Generating pair distance distributions from SAXS data using machine learning

Mr Ali Hussein (Niels Bohr Institute University of Copenhagen), Niklas Sørensen (Københavns Universitet)

19/08/2026, 16:00

Poster

Small-angle X-ray scattering (SAXS) is widely used to determine the structure of nanoparticles and biomolecules in solution. A central challenge is reconstructing the pair distance distribution function, $p(r)$, from the measured scattering intensity, $I(q)$. The $p(r)$ function describes the distribution of distances between scattering centers within a particle and provides valuable...