Development of Low-Cost Autonomous Systems

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dc.contributor.advisorTakeuchi, Ichiroen_US
dc.contributor.authorSaar, Logan Milesen_US
dc.contributor.departmentMaterial Science and Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2023-06-23T06:11:28Z
dc.date.available2023-06-23T06:11:28Z
dc.date.issued2023en_US
dc.description.abstractA central challenge of materials discovery for improved technologies arises from the increasing compositional, processing, and structural complexity involved when synthesizing hitherto unexplored material systems. Traditional Edisonian and combinatorial high-throughput methods have not been able to keep up with the exponential growth in potential materials and relevant property metrics. Autonomously operated Self-Driving Labs (SDLs) - guided by the optimal experiment design sub-field of machine learning, known as active learning - have arisen as promising candidates for intelligently searching these high-dimensional search spaces. In the fields of biology, pharmacology, and chemistry, these SDLs have allowed for expedited experimental discovery of new drugs, catalysts, and more. However, in material science, highly specialized workflows and bespoke robotics have limited the impact of SDLs and contributed to their exorbitant costs. In order to equip the next generation workforce of scientists and advanced manufacturers with the skills needed to coexist with, improve, and understand the benefits and limitations of these autonomous systems, a low-cost and modular SDL must be available to them. This thesis describes the development of such a system and its implementation in an undergraduate and graduate machine learning for materials science course. The low-cost SDL system developed is shown to be affordable for primary through graduate level adoption, and provides a hands-on method for simultaneously teaching active learning, robotics, measurement science, programming, and teamwork: all necessary skills for an autonomous compatible workforce. A novel hypothesis generation and validation active learning scheme is also demonstrated in the discovery of simple composition/acidity relationships.en_US
dc.identifierhttps://doi.org/10.13016/dspace/lpty-axr3
dc.identifier.urihttp://hdl.handle.net/1903/30033
dc.language.isoenen_US
dc.subject.pqcontrolledEducational technologyen_US
dc.subject.pqcontrolledArtificial intelligenceen_US
dc.subject.pqcontrolledMaterials Scienceen_US
dc.subject.pquncontrolledActive Learningen_US
dc.subject.pquncontrolledAffordableen_US
dc.subject.pquncontrolledAutonomousen_US
dc.subject.pquncontrolledEducationalen_US
dc.subject.pquncontrolledLEGOLASen_US
dc.subject.pquncontrolledMachine Learningen_US
dc.titleDevelopment of Low-Cost Autonomous Systemsen_US
dc.typeThesisen_US

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