Autonomous interpretation of atomistic scattering data

Abstract

Materials with bespoke properties have long been identified by computational searches, and their experimental realisation is now coming within reach through autonomous laboratories. Scattering experiments are central to verifying the atomic structures of autonomously synthesised materials. Yet, interpreting these measurements typically requires user expertise and manual processing, or machine learning (ML) models trained on predefined datasets, limiting fully autonomous materials discovery. Here, we introduce a differentiable optimisation framework that treats scattering calculations, energetics, and chemical constraints as a unified refinement problem. Capability demonstrations across molecules, crystal structures, nanoparticles, and amorphous matter show that this data-driven approach resolves structural degeneracies with multi-modal inputs - suggesting its usefulness for informing, and ultimately guiding, the operation of autonomous laboratories.