HELAO 🤖 🚀 🤝 👩‍🔬 👨‍🔬


Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high-throughput characterization, automatic analysis and machine learning. Within these MAPs one or multiple autonomous feedback loops aim to optimize materials for certain functional properties or generate new insights. Fundamentally, this necessitates accelerated, but foremost integrated, research actions. Herein, a web based asynchronous protocol to seamlessly integrate research tasks within a hierarchical experimental laboratory automation and orchestration (HELAO) framework is presented. We demonstrate the capability of orchestrating distributed research instruments that may incorporate data from experiments, simulations, and databases. HELAO offers interfacing laboratory hardware and software distributed across several computers and operating systems for executing an experiment, data analysis, provenance tracking, and autonomous planning. Research acceleration in terms of reduction of total experimental time is demonstrated to be close to 2x (in addition to the speedup of active learning of 5-10x depending on active learning metric) by deploying a centrally orchestrated fleet of instruments for a active learning. To the best of our knowledge, HELAO is the only laboratory automation framework with integrated data management capable of running closed loop optimization on multiple instruments and extreme modularity.

Fuzhan Rahmanian
Fuzhan Rahmanian
PhD Candidate at TUM

My research focuses on material acceleration and applied electrochemistry through sequential and machine learning algorithms. Different stages of my thesis compromise of hardware interfacing with python and visualization, using robots to perform AI accelerated experiments i.e. through active learning, benchmarking against linear models, and extracting the fundamental knowledge in reduced time over classical high-throughput experimentation for optimization of electrolyte formulations of post-Li ion battery systems.