Understanding materials through models, simulations and data.
Chair
Modelling
Models support design, virtual experiments, and comparison with experimental data.
Modelling approaches and computer simulations make it possible to design materials in the computer, test them in virtual experiments, and compare them directly with experimental data.
Data science
Incorporating domain knowledge remains an open question.
Statistical learning and deep-learning approaches are discussed with regard to their use in materials science. This includes the question of how domain knowledge from materials science and engineering can be incorporated into methods and models.
Research data
Research data management is increasingly relevant for data-intensive simulations and analyses.
Research data management and digitisation form a link between the Expert Committee’s topics. They are particularly relevant for data-intensive simulations and analyses.
The Expert Committee initiates and coordinates exchanges between doctoral researchers and experienced scientists from industry and academia. The focus is on materials modelling, computer simulation, data science, and research data.
The chair and deputy chair provide organisational and strategic direction, with advice from the Coordination Working Group.
The further development of modelling approaches and simulation methods makes it possible to design materials in the computer, study them in virtual experiments, and compare them directly with experimental data. This brings material properties and the role of the underlying microstructure into focus.
Methods from data science, including statistical learning and deep-learning approaches, complement this work. One open question is how domain knowledge from materials science and engineering can be incorporated into methods and models. Research data management and digitisation connect these topics, particularly in data-intensive simulations and analyses.
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