Image inversion for pattern formation

Image inversion of spinodal decomposition
Image inversion of spinodal decomposition governed by the Cahn-Hilliard equation.

Previously we showed the possibility of learning the physics of pattern formation from images. The method enables us to learn transient and nonequilibrium thermodynamic properties in a top-down and data-driven way. However the uncertainty of the inferred constitutive laws such as free energy, diffusivity, and reaction kinetics was unknown. In a recent follow-up paper, lead author and D3BATT team member Hongbo Zhao explored the inverse problem under different conditions such as limited temporal and spatial resolution, and used Bayesian inference to quantify the uncertainty. This work paves the way for applications in the inversion of noisy experimental images of pattern-forming systems.

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New battery lab commissioned at SLAC

In December 2020, Stanford/SLAC commissioned its new Battery Informatics Laboratory on the SLAC campus. In an effort to support the scientific need for experimental data on the cycling-performance and degradation of battery cells, the new lab hosts a number of high throughput state of the art cell cycling equipment. Using the instrument-cloud interface provided by BEEP (developed under D3BATT), the generated measurements are automatically validated, pre-processed and uploaded to an online servers for scientists from Stanford, MIT and TRI to analyze from the ‘comfort’ of their home offices.

The equipment includes:

  • Cylindrical cell testing with  192 channels capacity (Maccor Series 4000), operating in a convective controlled environmental chamber
  • Pouch cell testing with 192 channels capacity (Maccor Series 4000), including EIS measurements, operating in a convective controlled environmental chamber
  • Pouch cell testing with 128 channels capacity (BioLogic BCS-8xx), including EIS measurements, operating under liquid cooled cell holders
  • TBA: Novonix temperature chambers for calendar aging experiments