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Human-in-the-Loop Machine Learning for Automated Experiments


Invention Reference Number

Computer screen and scientist


Human-in-the-loop machine learning (hML) represents a paradigm that bridges the gap between autonomous AI systems and the indispensable intuition and creativity of human operators. In this model, a machine learning (ML) agent autonomously controls experimental workflows, while the human operator monitors and dynamically adjusts the ML agent's strategy. This method addresses the limitations of reinforcement learning in real-world applications by combining the speed and consistency of automation with the adaptive and innovative capabilities of human intelligence. By directly influencing the ML policies and objectives based on real-time observations and outcomes, this system ensures that experiments remain aligned with evolving scientific goals, thereby fostering discovery and optimization in material science, energy, and beyond.


  • Synergizes human insight with AI's processing power, optimizing experimental outcomes
  • Increases efficiency and reproducibility in scientific research and development
  • Facilitates the discovery of novel materials and solutions by integrating human creativity into automated processes

Applications and Industries

  • Materials science for discovery and optimization of new materials
  • Pharmaceutical industry for accelerated drug discovery
  • Materials science for developing efficient solar cells and batteries
  • Automation and robotics for enhanced decision-making processes

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