A 3-way inter-institutional cooperation on bridging scales from molecules to molecular materials by multiscale simulation and machine learning

Traditionally, the investigation of molecular mechanisms and the rational design of molecules and materials for targeted applications have been guided by physics-based modeling and simulation. While this has undoubtedly led to exciting discoveries and revolutionized modern science and technology, future advances are impeded by the need to map, explore, and analyse the infinitely complex and variable structure-property space of molecular systems across scales. This is where data-driven and machine learning methodologies, which have only recently begun to impact the field of molecular simulations, offer a promising approach. The combined use of data-driven and multiscale simulation-based approaches is still in its infancy but, due to recent advances in both techniques (e.g., deep learning, GPU-based simulation), promises large leaps forward in molecular and material design. To fulfil this promise, the major obstacles in applying data-driven methods to atomistic systems – treatment of complex three-dimensional structures, integration within multiscale simulation algorithms, to name just a few, – have to be resolved first.

The aim of the SIMPLAIX strategic research initiative is to pool the unique expertise at Heidelberg Institute for Theoretical Studies (HITS), Heidelberg University, and Karlsruhe Institute of Technology (KIT) in multiscale simulation – from quantum mechanics through classical mechanics to continuum modeling – on one hand and machine learning on the other to address this challenge. In SIMPLAIX, these techniques are being developed and employed to study a set of challenging problems in biomolecular and molecular material science in eight multidisciplinary, inter-institutional research projects.

SIMPLAIX is coordinated by HITS and started in October 2021. SIMPLAIX is funded by the Klaus Tschira Foundation and supported by in-kind contributions from KIT and Heidelberg University.


Here are some reasons:

  • A “simplex” is a generalized triangle, and this shape reflects the triangular (3-way, trilateral) collaboration between the 3 participating institutions.
  • SIM represents SIMulation
  • AI represents Artificial Intelligence
  • X represents the interdisciplinary eXchange between simulation, AI, molecules and materials.

And in case you wonder how to pronounce SIMPLAIX, it’s /sim’pleɪ.ks/ (IPA)

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