50 Experts at NREL Discuss How AI Can Help Bridge "Valley of Death"

Artificial intelligence (AI) could accelerate scientific discovery by helping researchers to more quickly gather data, search that data for patterns, and—eventually—generate insights that researchers might have missed.

Yet, leading experts in AI, materials science, chemistry, and robotics who gathered at the National Renewable Energy Laboratory emphasized that fully realizing the potential of autonomous experimentation requires not only speeding scientific discovery but also reshaping the entire research-to-industry pipeline.

In May 2025, NREL convened the Autonomous Research for Real-World Science (ARROWS) workshop. The event brought together more than 50 leaders in materials science, chemistry, AI, and robotics to consider how autonomous systems could help overcome long-standing bottlenecks in scientific discovery and translation to industry.

Through presentations, lab tours, and collaborative discussions, participants identified new opportunities for collaboration—and surfaced critical challenges that must be addressed to make autonomous science widely useful.

At the workshop, discussions converged on four key pillars that are needed to make AI an impactful partner in the lab.

• Metrics for Real-World Impact: Developing new AI reward functions and metrics that emphasize cost, manufacturability, and resource efficiency.

• Intelligent Tools for Causal Understanding: Shifting from correlation-focused machine learning toward causal models that provide deep, physics-based insights.

• Modular, Interoperable Infrastructure: Overcoming barriers posed by legacy equipment and proprietary data formats through modular workflows and standardized platforms for data sharing.

• Closing the Loop from Theory to Manufacturing: Using agent-based AI models to connect theory, synthesis, characterization, and scale-up in a continuous learning cycle.

Workshop attendees are drafting an upcoming scientific article to detail the largest opportunities for autonomous science and to provide a comprehensive roadmap for researchers, industry partners, and policymakers aiming to harness autonomous experimentation. Read the full article>>>