Artificial Intelligence powered Multifunctional Material Design

Creating sustainable energy storage solutions and at the same time producing novel materials for regenerative medicine: Both is possible when using the right supramolecular chemical compounds. The TUM Innovation Network for Artificial Intelligence powered Multifunctional Material Design (ARTEMIS) is aiming at the guided discovery of such molecules and at developing them as a unique toolbox for different applications, using Machine Learning and Additive Manufacturing.

Potential applications range from electrocatalysis for hydrogen production to guided tissue regeneration and ‘smart’ coating of medical implants. Data-driven prediction represents a novel and powerful way to boost the discovery, synthesis and the design of new multi-functional materials, as well as for scaling-up and fabrication of devices.

Our Team

Doctoral theses

  • Electrochemistry based sensor array with integration of MOFs (Beatrice De Chiara)
  • Identifying a MOF Platform for Applications in Electrochemistry and Regenerative Medicine (Sarah Dummert)
  • Graph Neural Networks for Quantum Chemistry (Nicholas Gao)
  • Development and characterization of biohybrid cardiovascular implants (Salma Mansi)
  • Electrocatalysis Functional Nanomaterials (Peter Schneider)
  • Data-driven collective variables discovery for advance sampling using metadynamics (Gohar Ali Siddiqui)
  • Computational methods and machine learning to optimize drug discovery and materials design (Julia Stebani)
  • Data-driven modelling of coarse-grained molecular dynamics and generative modelling (Maximilian Stupp)
  • Poly(vinylphosphonate)-based Therapeutics (Philipp Weingarten)


  1. B. Thakur, V.V. Karve, D.T. Sun, A.L. Semrau, L.J.K. Weiß, L. Grob, R.A. Fischer, W.L. Queen, B. Wolfrum: An Investigation into the Intrinsic Peroxidase‐Like Activity of Fe-MOFs and Fe‐MOFs/Polymer Composites. Advanced Materials Technologies, 2021, 6 (5), 2001048.
  2. S. Zips, L. Hiendlmeier, L.J.K. Weiß, H. Url, T.F. Teshima, R. Schmid, M. Eblenkamp, P. Mela, B. Wolfrum*: Biocompatible, Flexible, and Oxygen-Permeable Silicone-Hydrogel Material for Stereolithographic Printing of Microfluidic Lab-On-A-Chip and Cell-Culture Devices. ACS Applied Polymer Materials, 2021, 3 (1), 243-258. A Machine Learning Approach to Analyze the Surface Properties of Biological Materials.
  3. C. Rickert, E.N. Hayta, D.M. Selle, I. Kouroudis, M. Harth, A. Gagliardi, O. Lieleg. ACS Biomaterials Science & Engineering, 2021, 7, 4614–4625.
  4. Thakur, B.; Karve, V. V.; Sun, D. T.; Semrau, A. L.; Weiss, L. J. K.; Grob, L.; Fischer, R. A.; Queen, W. L.; Wolfrum, B., An Investigation into the Intrinsic Peroxidase-Like Activity of Fe-MOFs and Fe-MOFs/Polymer Composites. Advanced Materials Technologies 2021, 6 (5).
  5. L. Katzenmeier, M. Gößwein, A. Gagliardi, and A. S. Bandarenka. Modeling of Space-Charge Layers in Solid-State Electrolytes : A Kinetic Monte Carlo Approach and Its Validation. J. Phys. Chem. C 2022, 126, 26, 10900-10909.
  6. F. Mayr, M. Harth, I. Kouroudis, M. Rinderle, and A. Gagliardi. Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy. J. Phys. Chem. Lett. 2022, 13, 8, 1940–1951.
  7. Bio-Based and Bio-Inspired Adhesives from Animals and Plants for Biomedical Applications. T.M. Lutz, C. Kimna, A. Casini, O. Lieleg, Materials Today Bio, 2022, 13, 100203
  8. Materials from Cyclodextrin Metal-Organic Frameworks: Recent Developments and Applications. S. V. Dummert, H. Saini, Z. Hussain, K.Yadava, J.Kolleboyina, A. Casini, R. A. Fischer, ChemSocRev, 2022, 51, 5175–5213.
  9. Kollmannsberger, K. L.; Kronthaler, L.; Jinschek, J. R.; Fischer, R. A., Defined metal atom aggregates precisely incorporated into metal-organic frameworks. Chemical Society Reviews 2022, 51 (24), 9933-9959.
  10. Hussain, M. Z.; Yang, Z. X.; van der Linden, B.; Heinz, W. R.; Bahri, M.; Ersen, O.; Jia, Q. L.; Fischer, R. A.; Zhu, Y. Q.; Xia, Y. D., MOF-Derived Multi-heterostructured Composites for Enhanced Photocatalytic Hydrogen Evolution: Deciphering the Roles of Different Components. Energy & Fuels 2022, 36 (19), 12212-12225.
  11. Kaussler, C.; Wragg, D.; Schmidt, C.; Moreno-Alcantar, G.; Jandl, C.; Stephan, J.; Fischer, R. A.; Leoni, S.; Casini, A.; Bonsignore, R., “Dynamical Docking” of Cyclic Dinuclear Au(I) Bis-N-heterocyclic Complexes Facilitates Their Binding to GQuadruplexes. Inorganic Chemistry 2022, 61 (50), 20405-20423.
  12. Hussain, M. Z.; Grossmann, P. F.; Kohler, F.; Kratky, T.; Kronthaler, L.; van der Linden, B.; Rodewald, K.; Rieger, B.; Fischer, R. A.; Xia, Y. D., 3D-Printed Metal-Organic Framework-Derived Composites for Enhanced Photocatalytic Hydrogen Generation. Solar Rrl 2022, 6 (10).
  13. Machine Learning Approaches for Biomolecular, Biophysical and Biomaterials Research. C. Rickert, O. Lieleg, Biophysics Rev. 2022, 3, 021306.
  14. PET imaging of self-assembled 18F-labelled Pd2L4 metallacages for anticancer drug delivery, R. Cosialls, C. Simo', S. Borros, V. Gçomez-Vallejo, C. Schmidt, J. Llop, A. B. Cuenca, A. Casini, Chemistry Eur. J. 2022, 28, e202201575.
  15. Alkali metal cations change the hydrogen evolution reaction mechanisms at Ptelectrodes in alkaline media, Y. Taji, A. Zagalskaya, I. Evazzade, S. Watzele, K.-T. Song, S. Xue, C. Schott, B. Garlyyev, V. Alexandrov, E. Gubanova, A.S. Bandarenka, 2022, Nano Materials Science in press.
  16. Self-supervised optimization of random material microstructures in the small-data regime, M. Rixner, P-S Koutsourelakis, npj Computational Materials, Volume 8, 2022.
  17. S. Thomas, F. Mayr and A. Gagliardi. Adsorption and Sensing Properties of SF6 Decomposed Gases on Mg-MOF-74. Solid State Communications, 2023, 363, 115120.
  18. G. Moreno-Alcantar, A. Casini, Bioinorganic Supramolecular Coordination Complexes and Their Biomedical Applications, FEBS Letters, 2023, 597, 191-202.
  19. T. Tabish, M.Z. Hussain, R.A. Fischer, A. Casini, Mitochondria-targeted metal-organic frameworks for cancer treatment, Materials Today, 2023, accepted. (03.04.23), Ms#MATTOD-D-22-01279R2.
  20. S. Thomas, F. Mayr, A. Kulangara Madam and A. Gagliardi, Machine learning and DFT investigation of CO, CO2 and CH4 adsorption on pristine and defective two-dimensional magnesene, Physical Chemistry Chemical Physics, 2023, in press.
  21. N. Gao, S. Günnemann, Ab-initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions, ICLR 2022.
  22. N. Franco, T. Wollschläger, N. Gao, J.M. Lorenz, S. Günnemann, Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm, IEEE Conference on Quantum Computing and Engineering 2022.
  23. N. Gao, S. Günnemann, Sampling-free Inference for Ab-Initio Potential Energy Surface Networks, ICLR 2023.
  24. Kilian Maria Arthur Mueller, Salma Mansi and Petra Mela, Recent Advances in Melt Electrowriting for Cardiovascular Applications. (chapter book) 2023.