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ICTP Announces 2026 Dirac Medal Recipients (Physics)
ICTP has awarded its 2026 Dirac Medal to four physicists who have made important contributions to statistical mechanics, a framework used by physicists to describe the collective behaviour of systems with many constituents, and have used it to explore far-ranging concepts in theoretical physics with implications for real-world advancements, from the study of biological systems to that of financial markets and to artificial intelligence.
The Medal citation reads: "For their pioneering contributions to equilibrium statistical mechanics and for extending its concepts and methods into non-equilibrium statistical mechanics, optimization problems, theoretical neuroscience, and, finally, artificial intelligence".
“The work of the 2026 Dirac Medallists has contributed to establishing theoretical physics, and particularly statistical mechanics, as a powerful framework to tackle a very broad range of questions that go far beyond its traditional domain of interest to encompass biology, computer science and artificial intelligence,” commented ICTP Director Atish Dabholkar who chairs the prize committee. “I am particularly happy that this year’s Medal recognises a research field that has had a long tradition here at ICTP, rooted in part in the work of former ICTP Director Miguel Virasoro, and I warmly congratulate the four winners.”
ICTP Research Scientist Jean Barbier explains what statistical mechanics is and introduces us to the ideas behind the contributions of this year's medallists in this video:
The 2026 Dirac Medallists contributed to understanding different aspects of complex systems using statistical mechanics, with far-ranging applications.
Bernard Derrida developed mathematical models that explain how collective behaviour arises in complex disordered systems. For example, through his Random Energy Model, he was able to show that systems of many constituents can sometimes become dominated by a small number of favourable configurations. Although the model was developed for spin glasses, which originally described disordered magnetic alloys, its ideas have influenced fields ranging from optimisation and neural networks to artificial intelligence.
Deepak Dhar showed how simple interactions can lead to complex and sometimes unpredictable behaviour in large systems. Through his pioneering work on the sandpile model, for example, he described a familiar phenomenon: as grains are added to a sandpile, most result in no change or only small avalanches, while occasionally a single grain can cause a much larger collapse. The theory shows that the sandpile, like many other complex systems, spontaneously places itself in a critical regime where small effects can sometimes trigger large abrupt events and has been used to model systems as apparently different as earthquakes, traffic jams and fluctuations in financial markets.
Marc Mézard helped explain how disordered systems with a high degree of frustration emerging from many competing possibilities behave. One of his most important contributions is the cavity method, developed together with Giorgio Parisi and Miguel Virasoro, which provides a powerful and intuitive framework to describe a broad range of disordered systems, and yields practical algorithms to find their favourable configurations. These algorithms and theoretical ideas are applied in computer science, communication, optimisation, and, more recently, artificial intelligence.
Haim Sompolinsky has used statistical mechanics to understand the behaviour of neural circuits, memory and the brain, and pioneered the field of theoretical and computational neuroscience. While his first contributions regarded the study of spin glasses, early in his career he solved the Hopfield model, a simple mathematical model of memory. He used it to develop a statistical mechanical description of neural networks, which constitutes one of the first rigorous links between statistical physics and brain function, shedding light on the workings of a