CECAM-Lorentz Joint Workshop: Integrating Molecular Simulation with Machine Learning/Artificial Intelligence for Advance Material Design
- Frank Noe (Free University of Berlin, Germany)
- Siewert-Jan Marrink (University of Groningen, The Netherlands)
- Shirin Faraji (University of Groningen, The Netherlands)
- Niels Taatgen (University of Groningen, The Netherlands)
Efficient design of advanced materials, such as smart energy materials, cognitive materials, bio-inspired materials, etc, requires the use of dedicated multiscale simulation methods spanning the range from atoms all the way to the continuum level. Due to continuous improvement in hardware and software performance, state-of-the-art modeling approaches can nowadays be used in hight hroughput studies to aid in the rational design of novel materials. However, the vast space of model parameters on the one hand, and on chemical building blocks on the other hand warrants the use of smart methods to deal with the big amount of data that either goes into such models or results from the predictions. Here the use of machine learning techniques and methods borrowed from the realm of artificial intelligence are needed to bring the field of computational material design ahead. We envision that establishing such an interactive scientific network will serve as a solid foundation toward the ultimate goal of developing novel materials needed for efficient neuromorphic computers, improved photovoltaics, or biomedical applications to name but a few.