no code implementations • 10 Nov 2023 • A. Gilad Kusne, Austin McDannald, Brian DeCost
Autonomous materials research labs require the ability to combine and learn from diverse data streams.
no code implementations • 17 Jun 2023 • Felix Adams, Austin McDannald, Ichiro Takeuchi, A. Gilad Kusne
Here, we present a set of methods for integrating human input into an autonomous materials exploration campaign for composition-structure phase mapping.
1 code implementation • 19 Aug 2022 • A. Gilad Kusne, Austin McDannald
We demonstrate this framework with an autonomous material science lab in mind - where information from diverse research campaigns can be combined to ad-dress the scientific question at hand.
no code implementations • 12 Apr 2022 • Alex Wang, Haotong Liang, Austin McDannald, Ichiro Takeuchi, A. Gilad Kusne
In these systems, machine learning controls experiment design, execution, and analysis in a closed loop.
no code implementations • 8 Apr 2022 • Logan Saar, Haotong Liang, Alex Wang, Austin McDannald, Efrain Rodriguez, Ichiro Takeuchi, A. Gilad Kusne
We present the next generation in science education, a kit for building a low-cost autonomous scientist.
no code implementations • 15 Nov 2021 • A. Gilad Kusne, Austin McDannald, Brian DeCost, Corey Oses, Cormac Toher, Stefano Curtarolo, Apurva Mehta, Ichiro Takeuchi
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades.