At Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences, Yongtao Liu is building AI-driven “closed-loop” nanomaterials experiments that can plan measurements, interpret results in real time and choose the next step — accelerating discovery without removing human judgment. His focus is not just speed but trustworthy autonomy: systems must be interpretable, resilient to instrument artifacts and designed to avoid “false novelty,” where noise masquerades as new physics. Drawing on work such as novelty detection in conductive AFM studies of halide perovskites — linking local microstructure to unusual hysteresis behavior — Liu emphasizes that autonomous labs also demand better methods to validate and understand the massive data they produce. He is developing practical tools like AEcroscopy to standardize automated microscopy workflows and a Gated Active Learning Framework to prevent models from confidently learning from out-of-assumption data, while also pushing cross-facility autonomy that links fast measurements with slower synthesis. Ultimately, Liu envisions AI that helps scientists reason and explore vast experimental spaces — freeing researchers from repetitive tasks so they can focus on asking sharper questions.

Academics are investigating whether coping with the unknown or unexpected is a distinct contributor to anxiety in autistic adults.

Disposing of wastewater treatment plant waste while simultaneously recovering white phosphorus: The new thermochemical FlashPhos process demonstrates how this works. It is currently the only technology in Europe capable of producing this essential raw material for industrial use in an environmentally friendly, efficient, and cost-effective way. A Europe-wide consortium, coordinated by the University of Stuttgart, has developed and studied the technology.