Virtual Material Design



  • Tremolo-X/ATK-ForceField, for numerical simulation in molecular dynamics
  • HCFFT, for efficient treatment of high-dimensional multivariate functions

Our Services

  • Data-driven virtual material design
  • Mathematical modelling
  • Developing and implementation of specialized algorithms
  • Customized solutions

Quantum computing

Quantum computers promise to solve certain problems orders of magnitude faster than conventional digital computers. However, this requires new algorithms and solution methods.


  • Multiscale modeling and numerical simulation for material science, chemistry and nanotechnology
  • Machine learning, high-dimensional problems, optimization, and data analysis

Progress in industry is driven by innovative materials with special properties. Materials science, chemistry, and nanotechnology are thus among the key technologies of the current century.

The basis for the design of new materials and molecules is an understanding of their properties on the nanoscale and their corresponding behavior on the macroscale. Multiscale models in both space and time are therefore necessary to describe a material accurately and to predict its long-term behavior.

Our objective is to create, optimize, and study novel materials and molecules in the virtual laboratory in order to assist the practical development process through a substancial cost reduction, both in terms of time and money.

Applied research and development at VMD focuses on data-driven design, multiscale modelling, and efficient simulation techniques. We apply state-of-the-art numerical methods for high-dimensional problems, optimization, machine learning, and (big) data analysis.

By combining these techniques with in-house expertise, empirical data, and custom-built software, we aim to pioneer a comprehensive design approach for new materials.

Data-driven design

Traditionally, there have been two scientific paradigms, the empirical (i.e. experimental) and the model-based (i.e. theoretical) approach. At the turn of the century, computational science established itself as a third paradigm. The current prevalence of experimental and simulated data has led to the rise of data-driven science, which can be considered a "fourth paradigm".

A modern approach to a scientific or engineering question may proceed with a "whitebox" model, consisting of an exact physical description of the modeled system (e.g. through solutions of PDEs). Alternatively, a "blackbox" model might be employed, examining only the input/output behaviour of a system (through, e.g. machine learning techniques). If results are unsatisfactory, so-called intermediate "graybox" models can be developed.