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Autonomous Materials Exploration Integrates Automated Phase Identification and AI Agents Enhanced by Human Guidance

Ming-Chiang Chang1,*,†, Maximilian Amsler1,*,‡, Duncan R. Sutherland1, Sebastian Ament2, Katie R. Gann1, Lan Zhou3,4, Louisa M. Smieska5, Arthur R. Woll5, John M. Gregoire3,4 et al.

Carla P. Gomes2, R. Bruce van Dover1, and Michael O. Thompson1,§

  • *These authors contributed equally to this work.
  • †Contact author: mc2663@cornell.edu
  • ‡Contact author: amsler.max@gmail.com
  • §Contact author: mot1@cornell.edu
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PRX Intelligence 1, 013019 – Published 10 September, 2026

DOI: https://doi.org/10.1103/c362-349b

Abstract

Autonomous experimentation holds the potential to accelerate materials development by combining artificial intelligence (AI) with modular robotic platforms to explore extensive combinatorial chemical and processing spaces. Such self-driving laboratories can not only increase the throughput of repetitive experiments, but also incorporate human domain expertise to drive the search toward user-defined objectives, including improved materials performance metrics. We present an autonomous materials synthesis extension to SARA, the Scientific Autonomous Reasoning Agent, utilizing phase information provided by an automated probabilistic phase labeling algorithm to expedite the search for targeted phase regions. By incorporating human input into an expanded SARA-H (SARA with human-in-the-loop) framework, we enhance the efficiency of the underlying reasoning process. Using synthetic benchmarks, we demonstrate the efficiency of our AI implementation and show that the human input can contribute to significant improvement in sampling efficiency. We conduct experimental active learning campaigns using robotic processing of thin-film samples of several oxide material systems, including Bi2O3, SnOx, and Bi−Ti−O, using lateral-gradient laser spike annealing to synthesize and kinetically trap metastable phases. We showcase the utility of human-in-the-loop autonomous experimentation for the Bi−Ti−O system, where we identify extensive processing domains that stabilize δ−Bi2O3 and Bi2Ti2O7, explore dwell-dependent ternary oxide phase behavior, and provide evidence confirming predictions that cationic substitutional doping of TiO2 with Bi inhibits the unfavorable transformation of the metastable anatase to the ground-state rutile phase. The autonomous methods we have developed enable the discovery of new materials and new understanding of materials synthesis and properties.

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PRX Intelligence 1, 013019– Published 10 September, 2026
  • Received 17 April 2026
  • Accepted 22 July 2026
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