Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping. - A. Gilad Kusne; Austin McDannald; Brian L. DeCost; Corey Oses; Cormac Toher; Stefano Curtarolo; Apurva Mehta; Ichiro Takeuchi (CoRR, 2021)

A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation. - Logan Saar; Haotong Liang; Alex Wang; Austin McDannald; Efrain Rodriguez; Ichiro Takeuchi; A. Gilad Kusne (CoRR, 2022)

Benchmarking Active Learning Strategies for Materials Optimization and Discovery. - Alex Wang; Haotong Liang; Austin McDannald; Ichiro Takeuchi; A. Gilad Kusne (CoRR, 2022)

Scalable Multi-Agent Framework for Optimizing the Lab and Warehouse. - A. Gilad Kusne; Austin McDannald (CoRR, 2022)

Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping. - Felix Adams; Austin McDannald; Ichiro Takeuchi; A. Gilad Kusne (CoRR, 2023)

Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning. - A. Gilad Kusne; Austin McDannald; Brian DeCost (CoRR, 2023)

Real-time experiment-theory closed-loop interaction for autonomous materials science. - Haotong Liang; Chuangye Wang; Heshan Yu; Dylan Kirsch; Rohit Pant; Austin McDannald; A. Gilad Kusne; Ji-Cheng Zhao; Ichiro Takeuchi (CoRR, 2024)