Adaptive AI-Enhanced Scientific Experimentation Using Real-Time Simulation Feedback

Research Question: Can GPT-5, integrated with real-time hardware-in-the-loop (HIL) simulation environments, autonomously design and adapt experiments to accelerate scientific discovery in domains such as materials science or synthetic biology?

Hypothesis: AI-driven, real-time adaptive experimentation will outperform static experimental protocols in both efficiency and discovery rate by leveraging continuous feedback and optimization.

Experiment Plan: Set up a real-time simulation environment (e.g., smart grid or catalytic materials discovery testbed). Integrate GPT-5 as an experiment planning and adaptation agent, receiving live data streams. Compare outcomes (speed, novelty, reliability) of AI-adaptive vs. human-designed and traditional automated protocols. Evaluate the system’s ability to recognize and exploit unexpected experimental results.

References:

• Montoya, J., Brandl, R., Vishwanath, K., Johnson, J., Darbali-Zamora, R., Summers, A., Hashimoto, J., Kikusato, H., Ustun, T., Ninad, N., Apablaza-Arancibia, E., Bérard, J.-P., Rivard, M., Ali, S. Q., Obushevs, A., Heussen, K., Stanev, R., Guillo-Sansano, E., Syed, M., Burt, G., Cho, C., Yoo, H., Awasthi, C., Wadhwa, K., & Bründlinger, R. (2020). Advanced Laboratory Testing Methods Using Real-Time Simulation and Hardware-in-the-Loop Techniques: A Survey of Smart Grid International Research Facility Network Activities. Energies.
• Hosseini-Ardali, S. M., Ragab, A., Amazouz, M., & Yacout, S. (2024). AI-Assisted Acceleration of Catalytic Materials Design/Discovery: Towards achieving net-zero emissions. IISE Annual Conference & Expo 2024.