Governing The Accelerator: The Case for National Collaboration in AI‐Enabled Materials Discovery
Abstract
Artificial intelligence (AI) is reshaping materials discovery into a more integrated pipeline by combining data generation, surrogate prediction, and, in selected settings, closed‐loop experimentation at an unprecedented rate. However, the institutional mechanisms needed to govern reproducibility, interoperability, and oversight across these workflows remain underdeveloped. This perspective examines how federally funded research and development centers (FFRDCs) can close that gap across three domains: data and infrastructure, algorithmic intelligence, and autonomous experimentation. In each, absent a national coordinating body, these decisions default to institutions not built for coordination, accountability, or mission persistence. We propose a two‐tier leadership model in which science and technology (S&T) laboratories drive technical capability, while study and analysis (S&A) centers provide policy integration, cost‐benefit analysis, and governance frameworks to translate scientific output into national strategy. Without a coordinating framework, the United States risks advancing a technology faster than it can govern it. Meanwhile, foreign competitors pursue centralized national materials programs of their own.
