NeuDiff Agent: A Governed AI Workflow for Single-Crystal Neutron Crystallography
🤖 AI Summary
This study addresses the inefficiencies in structural and magnetic characterization at large-scale neutron facilities, where cumbersome analysis workflows often delay reporting and hinder scientific productivity. To overcome this, the authors propose a trustworthy AI agent architecture based on large language models, which automates the entire processing pipeline—from data reduction, integration, and refinement to validation—by integrating controlled tool invocation, verification gates at critical decision points, and full provenance tracking. The system directly produces publication-ready CIF files validated to contain no checkCIF A- or B-level alerts. Benchmark evaluations demonstrate that the approach reduces total processing time from 435 minutes to 86–94 minutes, achieving a 4.6–5.0× speedup while maintaining rigorous reliability, thereby significantly enhancing scientific throughput.
Application Category
📝 Abstract
Large-scale facilities increasingly face analysis and reporting latency as the limiting step in scientific throughput, particularly for structurally and magnetically complex samples that require iterative reduction, integration, refinement, and validation. To improve time-to-result and analysis efficiency, NeuDiff Agent is introduced as a governed, tool-using AI workflow for TOPAZ at the Spallation Neutron Source that takes instrument data products through reduction, integration, refinement, and validation to a validated crystal structure and a publication-ready CIF. NeuDiff Agent executes this established pipeline under explicit governance by restricting actions to allowlisted tools, enforcing fail-closed verification gates at key workflow boundaries, and capturing complete provenance for inspection, auditing, and controlled replay. Performance is assessed using a fixed prompt protocol and repeated end-to-end runs with two large language model backends, with user and machine time partitioned and intervention burden and recovery behaviors quantified under gating. In a reference-case benchmark, NeuDiff Agent reduces wall time from 435 minutes (manual) to 86.5(4.7) to 94.4(3.5) minutes (4.6-5.0x faster) while producing a validated CIF with no checkCIF level A or B alerts. These results establish a practical route to deploy agentic AI in facility crystallography while preserving traceability and publication-facing validation requirements.
Problem
Research questions and friction points this paper is trying to address.
neutron crystallography
analysis latency
scientific throughput
single-crystal
data validation
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI workflow
governed agent
neutron crystallography
automated structure validation
provenance tracking
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