Theoretical modelling

Getting to grips with complexity

Professorerna Igor Abrikosov and Tien Son Nguyen — Igor Abrikosov, Professor in Theoretical Physics and Tien Son Nguyen, Professor in Semiconductor Materials. Photographer: Charlotte Perhammar

Advanced simulations and models are used to get to grips with complex problems. Research in theoretical modelling at LiU covers a very wide field. Researchers study everything from molecular motors and patient-specific drug treatments to processes in biological ecosystems and how the universe functions.

Theoretical modelling is becoming an increasingly important research area. Realistic simulations of complex systems can be used to supplement practical experiments, which are often both expensive and time-consuming. In some cases, mathematical models and simulations can completely replace experiments that may be dangerous, or experiments within geophysics and astrophysics that are extremely difficult or impossible to carry out.

New and exciting possibilities are now available to solve several important problems in a realistic way. Rapid developments in computer technology, efficient calculation algorithms, computer programs and databases filled with information give rise to an increased need for research and education in theory and modelling.

At LiU, research is carried out within bioinformatics, theoretical physics, theoretical chemistry, theoretical biology, and theory and modelling in organic electronics.

The National Supercomputer Centre (NSC) at Linköping University is also a provider of leading edge national supercomputing resources.

Research

Theory and modelling for organic electronics

The theoretical simulation and modelling of the basic properties of organic materials and devices represents the main focus of the research activity of our group.

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FunMat-II

FunMat-II is a second generation competence center in material science, focusing its efforts to the areas of functional surfaces for cutting tools, fuel cells and batteries.

Mathematical Modelling of the Blood Circulatory System

Mathematical models of the circulatory system can be used in order to better understand the system and so help diagnose blood-vascular diseases and other problems while also enabling planning for the treatment of these diseases.

Ecological networks and community ecology

We use ecological networks and other modelling approaches to address a variety of community ecology questions.

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Sustainable nutrient and energy management

Natural resources are the building blocks of any eco system. Understanding where these resources are, how they flow on our landscapes, and why we see these use patterns are key questions to inform more sustainable management.

Nanoscale Engineering

Our vision is to generate knowledge that will trigger a paradigm shift with respect to the way contemporary materials are created.

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Forum Scientium Graduate School

Graduate school Forum Scientium, a bridge between medicine, natural sciences and engineering, stimulates creativity and a common use of ideas, knowledge, and equipment.

Translational bioinformatics

Many currently used drugs are ineffective for treating complex diseases. However, modern biology today generates enormous amounts of inexpensive, accurate, high-throughput data (‘omics), at several molecular levels.§

Computational code frog and network system.

Research in ecological and environmental modelling

Our research uses mathematical, computational, and statistical modeling techniques to integrate knowledge from large datasets across a variety of specialities.

News

International collaboration lays the foundation for AI for materials

AI is accelerating the development of new materials. Large-scale use and exchange of data on materials is facilitated by a broad international standard. A major international collaboration now presents an extended version of the OPTIMADE standard.

How to shift gears in a molecular motor

Scientists have long strived to develop artificial molecular motors that can convert energy into directed motion. Researchers at LiU have now presented a solution to a challenging problem: a “molecular gear”.

Severe MS predicted using machine learning

A combination of only 11 proteins can predict long-term disability outcomes in multiple sclerosis (MS) for different individuals. The proteins could be used to tailor treatments to the individual based on the expected severity of the disease.