Engineered living materials (ELM) are rapidly evolving, offering novel opportunities in multiple sectors such as biomedicine, sensorics, or construction [1]. Given the large design parameter space in the development of ELMs, current syntheses are a time-consuming process requiring a researcher’s knowledge and intuition to determine the right composition and processing conditions to finally create a living material with desired functionality and properties. To accelerate the development of new ELMs, there is a need to transform this process from an “artisanal science” into a data-based engineering discipline. To this aim, we here propose an accelerated development cycle combining automated, multiparallel living material synthesis and characterization with AI-based analysis and prediction tools.

This concept of materials development is demonstrated by a composite material based on wood particles, agricultural products, and engineered microorganisms expressing proteins that mediate cohesion of the material components. Using automated solid and liquid handling, a combinatorial library of 420 material formulations was constructed, characterized with the help of a robotic arm for sample handling [2] and used to train the TabPFN [3] AI-based model. Using inverse design strategies, we predicted material formulations with desired properties exemplified by the material’s Young’s modulus. We used optimized formulations to manufacture prototypes such as furniture components.

We expect our approach to be scalable to other ELM formulations and application scenarios to accelerate the development of ELMs and support projects in transitioning from concept to practical application.

[1] O. Burgos-Morales, M. Gueye, L. Lacombe, C. Nowak, R. Schmachtenberg, M. Hörner, C. Jerez-Longres, H. Mohsenin, H.J. Wagner, W. Weber Materials Today Bio, 2021, 11, 100115

[2] S. Conrad, P. Auth, T. Masselter, T. Speck Advanced Intelligent Systems, 2025, 2401086

[3] N. Hollmann, S. Müller, L. Purucker, A. Krishnakumar, M. Körfer, S. Bin Hoo, S, R. T. Schirrmeister, F. Hutter Nature, 2025, 637, 319