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CHRNS Non-Equilibrium Structure of Materials Initiative

Summary

 

With the advent of vSANS, CANDOR, and MACS, which provide accelerated data rates, and the deployment of the new, more flexible, data acquisition system at NIST, coupled with the rise of machine learning and AI, CHRNS is well-positioned to supply the advanced neutron capabilities required to characterize materials out of equilibrium. Through this initiative, CHRNS will be able to offer the academic community access to exceptional neutron scattering capabilities to address pressing scientific issues.

Description

CHRNS Kinetics Initiative

High level composition of the Kinetics Initiative

The MACS, vSANS, and CANDOR instruments will be upgraded to allow new approaches for time- resolved experiments.

New hardware will allow CHRNS users to remotely reduce and analyze their data.

New data formats that incorporate the multiple time-resolved data streams and a new methodology of maintaining the data in an accessible database will be implemented.

Software will be implemented for experimental optimization on CANDOR and vSANS.

A combination of computational power and software tools will be developed to treat the time-stamped data in almost real time for immediate user feedback and AI-based experimental control.

Time-stamping of the instrumental configuration and constant sample rotation will increase the effective data rates on MACS.

New electrochemical and stopped-flow capabilities will be developed

Unscheduled Outage Progress

With CHRNS efforts split over improving FAIR-data implementations here are the major milestones achieved during the outage

  • Resource loaded project management plans for FAIR-related activities
  • Rebalancing CHRNS Non-Equilibrium Structure of Materials Initiative to take advantage of the outage
  • Procurements on schedule:
    • Potentiostat received and passed acceptance testing
    • MACS rotational stage design and engineering
    • CANDOR liquid exchange system robotics components in procurement
    • All significant network hardware and computational resources (despite global supply chain issues)
  • Prototype sample-environment and instrument event-mode system
    • Server-client architecture
    • High-level Standards and Protocols defined
    • Version control of Labview libraries
    • Concepts for device discovery 
    • Concepts for including device and hardware information in data files for FAIR
  • 1-2 shear cell control redesign to work with event-mode and current NICE controls
    • Improved furnace and temperature control on 
    • Event-mode is to be expanded on other rheometers
  • In-house stopped-flow system
    • Proof-of-principle flow system with 3 sec  effective resolution
      • Instrument control scripts and rudimentary automatic data reduction/visualization
    • Engagement with key users and experts; feedback has been important in deciding priorities and capabilities
    • proof of principle
    • Improved temperature controlled sample environment 
  • Autonomous Formulations Lab (nSOFT)
    • Inspiration for CANDOR liquid exchange system
  • Autonomous data collection for NR
    • Demonstrate viability of autonomous experimentation, where data are analyzed in the context of a predetermined model in real time
  • Full McStas simulation of MACS 
    • allows full characterization of timing delays from sample to detector (~1 ms) with detection uncertainties
    • allows full characterization of resolution function for data fitting and physics model assignment
  • Estimates of timing resolution and delays between sample and detectors for Candor and MACS
  • Visualization of MACS event mode data using DAVE Mslice
  • CANDOR Biological reflectivity workflow
    • Transcribed C++ Library to Python (solid-supported lipid bilayer, membrane-associated protein, peptide, or small molecule)
    • Jupyter Notebook workflow for data fitting, v1.0
    • Improved functionality to Notebook (post-processing of fit, publication-ready graphs)
    • In progress to having web-based functionality; 
  • Bayesian optimization of experiments
    • from above workflow added support for experimental optimizations
    • Goal: complete with remote execution capabilities
  • Implementing experimental optimization methods for SANS
    • Synthetic data generation including measurement uncertainties for SANS underway
    • Coordination with SASView underway
    • Goal: complete with remote execution capabilities 
  • Remote fitting of SANS data in SASView
    • Multi-CPU and GPU support for remote fitting
    • Separation of computational code from GUI 
    • Staff Training Course Focused on writing new models and code structure

 

 

 

 

 

 

Created March 12, 2021, Updated January 7, 2022