Ilona Wagner

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About

Ilona Wagner is a pioneering researcher at the intersection of materials science and artificial intelligence, with a primary focus on the automated synthesis and optimization of metal–organic frameworks (MOFs). Her most cited work, "Fully Automated Optimization of Robot‐Based MOF Thin Film Growth via Machine Learning Approaches" (2022, 44 citations), represents a landmark contribution to the field. Wagner developed a fully automated, robot-driven platform that integrates machine learning algorithms to optimize the growth of MOF thin films—a critical step for integrating these porous crystalline materials into functional devices such as sensors, membranes, and catalytic systems. By replacing traditional trial-and-error methods with intelligent, data-driven experimentation, her approach dramatically accelerates the discovery of optimal synthesis conditions while reducing material waste. This work bridges the gap between high-throughput robotics and adaptive learning, setting a new standard for autonomous materials discovery. Wagner’s research is particularly notable for its practical impact on device integration, as MOF thin films are essential for real-world applications but notoriously difficult to fabricate reproducibly. Her innovative methodology has inspired a growing movement toward self-driving laboratories in materials chemistry, positioning her as a leader in the emerging field of AI-accelerated materials synthesis.