Machine learning to improve materials discovery and sensor technologies

Functional Material Genomics: Towards Physicochemical Databases for Covalent Organic Framework Synthesis and Digital Olfaction 

Prof. Gianaurelio Cuniberti, TU Dresden

Materials innovations enable new technological capabilities and drive major societal advancements but typically require long and costly development cycles. Materials Genomics aims at realizing the transition to a new paradigm in materials development from a traditional "trial and error" mode to a "rationally designed experiments" mode. The key element of this highly promising approach is the availability of materials data which can be searched and analyzed in order to understand structure – property relationships and to select new candidate materials for further investigation.

In this presentation I will discuss two examples which are currently under investigation. 

Covalent Organic Frameworks (COFs) have gained a lot of interest during the last years because of their potential application in several fields. The properties of the synthesized materials depend on the characteristics of the corresponding organic building blocks, which leads to nearly endless combinatorial possibilities. This complexity poses a formidable challenge for theory and simulations in order to guide the selection of precursor molecules. I will discuss our simulation efforts to predict electro-mechanical properties of two-dimensional COFs and their relation to the properties of the respective molecular building-blocks [1,2]. The results are based on more than 500 structures, most of which have been already experimentally synthesized.

Compared to the visual and auditory senses, which have long since successfully found their way into artificial intelligence applications, the sense of smell has been comparatively poorly understood. In order to change that, data analysis via artificial intelligence plays a crucial role: on the one hand, it is key for the intelligent discovery and prediction of complex chemical interactions of smelling substances. On the other hand, artificial intelligence is crucial for mimicking human perception of smells. In order to find relations between the structure of molecules and their perception, we use the concept of the physiochemical odor space which is built from 4094 molecular descriptors of 1389 odor molecules [3]. In a second study, we use liquid-phase-exfoliated functionalized graphene sensors to discriminate odor molecules based on their sensor fingerprint. Relating the fingerprints to the physicochemical space will allow further optimization of the sensor material towards digital olfaction.

[1]  A.Raptakis, A.Dianat, A.Croy, G.Cuniberti, Nanoscale, 2021, 13, 1077

[2]  A. Ortega-Guerrero, H. Sahabudeen, A. Croy, A. Dianat, R. Dong, X. Feng, G. Cuniberti, ACS Applied Materials & Interfaces, 2021, 13, 26411

[3]  A. L. Bierling, I. Croy, T. Hummel, G. Cuniberti, A. Croy, Brain Sciences, 2021, 11, 563

Brief Introduction of Speaker
Gianaurelio Cuniberti

Professor Gianaurelio Cuniberti holds since 2007 the Chair of Materials Science and Nanotechnology at the Technische Universität Dresden (TU Dresden) and the Max Bergmann Center of Biomaterials in Dresden, Germany. He is a member of the TU Dresden School of Engineering Sciences (Materials Science) and of the School Science (Physics). Since 2009, Professor Cuniberti is Honorary Professor at the Division of IT Convergence Engineering of the Pohang University of Science and Technology (POSTECH), since 2011 Adjunct Professor for the Department of Chemistry at the University of Alabama and Guest Professor at Shanghai Jiao Tong University since 2019. In 2018 he became faculty member of the transCampus between TU Dresden and King’s College London. Professor Cuniberti is an elected member of the European Academy of Sciences.
He studied Physics at the University of Genoa, Italy (where he got his B.Sc. and M.Sc.) and obtained his PhD in 1997 at the age of 27 in a joint collaboration between the University of Genoa and the University of Hamburg, Germany. Professor Cuniberti has made lasting contributions to a wide range of areas from quantum dots, nanowires and nanotubes to biosystems, addressing quantum transport phenomena, structural stability with important contributions to the theory and modeling of the electronic and structural properties of bottom up nanoscale materials. His R&D work addresses five main areas: (i) molecular and organic electronics, (ii) on-device digital sensing, (iii) nanostructured materials, (iv) methods development and (v) technology transfer, evaluation and commercialization. His research activity is internationally recognized in 403 scientific journal papers to date.