Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

Abstract

Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein.

Data Specifications for Battery Manufacturing Digitalization: Current Status, Challenges, and Opportunities

“…We believe that the present work paves the way towards concrete digitalization of battery manufacturing pilot lines and eventually including autonomous processes, which started to emerge in other fields [199,200] . This is because we think that our work provides a solid background to establish practical recipes and a list of good practices and recommendations on how to implement digital twins of battery prototyping lines.…”

“…We believe that the present work paves the way towards concrete digitalization of battery manufacturing pilot lines and eventually including autonomous processes, which started to emerge in other fields. [199,200] This is because we think that our work provides a solid background to establish practical recipes and a list of good practices and recommendations on how to implement digital twins of battery prototyping lines. It also constitutes a solid background for battery manufacturing data standardization and ontology initiatives such as the ones mentioned in the roadmap of the European Battery 2030 + research program or the ones that could be undertaken in future European projects.…”

Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling

“…LabView was used to orchestrate all devices-specific interfaces are detailed in Supplementary Table 1, and the code is available to the public, segregated into high-level orchestration VIs for runs and rinses and low-level control VIs as specified in the README. The autonomous experiment literature has already explored C++/ROS and Python options 30 , 31 for experiment management. LabView was selected for ease of development, management of multiple VISA connections via NI-MAX, and reliably low latency (single milliseconds) for triggering calls to these multiple VISA connections—this last point is crucial for Clio as high-pressure argon contacts many valves, pumps, and instruments in the rinse process.…”

Brokering between tenants for an international materials acceleration platform

“…It can handle incoming and outgoing requests asynchronously and acts as a central hub in the communication. In contrast to the laboratory orchestration system HELAO 30 , FINALES acts as a brokering service on a hardware independent level by abstracting away from a sequence of hardware events to requesting desired outcomes. FINALES allows for maximum flexibility and redundancy by explicitly allowing several hardware or software services with the same capabilities as well as a virtually unlimited number of simultaneous optimizers.…”

“…optimizers, experiments, simulation and databases exist in acooperative environment in which each participant can be located anywhere on the globe. The inset presents an emblematic workflow of a HELAO30 run in which multiple instruments can be pooled together but typically a run is limited to a single laboratory. The design of our MAP allows for the integration of a HELAO workflow.…”