Understanding voltage fade in Li-rich batteries

a schematic of the process used to bridge the gap

A team of researchers from this department, Engineering, Statistics and from the University of Basel have published a paper in Physical Review X* which explains how they used machine learning to close the 'reality gap' between predicted and observed behaviour from quantum devices.

The discrepancies between reality and simulation impede the optimisation and scalability of solid-state quantum devices.  Disorder induced by the unpredictable distribution of material defects is one of the major contributions to the reality gap.  

The authors bridged the gap using physics-aware machine learning - in particular using an approach combining a physical model, deep learning, Gaussian random field and Bayesian inference.  This approach enabled them to infer the disorder potential of a nanoscale electronic device from electron-transport data, which was validated by verifying the algorithm's predictions about the gate-voltage values required for a laterally defined quantum-dot device in AIGaAs/GaAs to produce current features corresponding to a double-quantum-dot regime.

You can read more about this work on the University's webpage, and download the full paper itself ('Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning'.).