Institute of Theoretical Informatics (ITI) - AiMat group


Machine learning enables simplified measurement of material properties for future autonomous laboratories.

The automation of scientific experiments with artificial intelligence has the potential to significantly accelerate the development and optimization of novel materials with tailored properties [1].

In a recently published article [2] in ACS Nano, the authors show that they can use neural networks to draw conclusions about the conductivity of polymer mixtures from comparatively easy to measure optical properties. The conductivity of polymers is more interesting, but also much more difficult to measure than optical properties. This principle can also be transferred to other materials and properties. The use of artificial intelligence methods therefore has the potential to optimize the numerous parameters available for the development of materials in faster and more cost-effective way by replacing complex measurement methods for determining material properties with rapid, e.g. optical experiments, which then use machine learning to predict the actual target property.

[1] Mission Innovation: Materials Acceleration Platform, Report of the Clean Energy Materials Innovation Challenge Expert Workshop, January 2018
[2] Loïc M Roch, Semion K Saikin, Florian Häse, Pascal Friederich, Randall H Goldsmith, Salvador León, Alán Aspuru-Guzik, From Absorption Spectra to Charge Transfer in Nanoaggregates of Oligomers with Machine Learning, ACS Nano 2020



Design of new catalysts by artificial intelligence.

CatalChemical Science titlepageysts are not only used to clean exhaust gases, but are also indispensable in the production of molecules and materials. The design of ever newer molecules and materials would be unthinkable without the simultaneous development of newer and better catalysts. In addition, novel catalysts also play a decisive role in recovering CO2 from the atmosphere and thus in combating climate change. A recent study [1] published in Chemical Science demonstrates how machine learning can be coupled with highly accurate but expensive simulation methods to predict the efficiency of new catalysts by computational methods and thus significantly accelerate their design. Pascal Friederich, newly appointed junior professor at KIT, and his co-authors show in the article that machine learning is not a black box but can be interpreted to derive design rules for new catalysts that are intelligible to scientists. The proposed method for the virtual design of new iridium catalysts can be transferred as desired to further classes of reactions of homogeneous (and in principle also heterogeneous) catalysis.

[1] Pascal Friederich, Gabriel dos Passos Gomes, Ricardo De Bin, Alán Aspuru-Guzik, David Balcells, Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex, Chemical Science, 2020, 11, 4584