Virtual Material Design



  • Tremolo-X/ATK-ForceField, for numerical simulation in molecular dynamics
  • HFFT, for efficient treatment of highdimensional multivariate functions

Our Services

  • data-driven virtual material design
  • mathematical modelling
  • developing and implementation of specialized algorithms
  • bespoke solutions

Areas of Research

  • Multiscale modelling and simulation in quantum mechanics, molecular dynamics and continuum mechanics
  • High-dimensional problems, optimization, and (big) data analysis
  • Computer-aided material and molecular design

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

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

Our objective is to create, otpimize, 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, modern multiscale modelling, and efficient simulation techniques. To this end, 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.


Innovative data-driven design


Traditionally, the two dominant scientific paradigms were empirical (i.e. experimental) and model-based (i.e. theoretical). Over the turn of the century, computational science became established 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 "white-box" model, consisting of an exact physical description of the modeled system (e.g. through solutions of PDEs). Alternatively, a "black-box" 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 "gray-box" models can be developed.