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Electrochemical phenomena in ferroelectrics are of particular interest for catalysis and sensing applications, with recent studies highlighting the combined role of the ferroelectric polarisation, applied surface voltage and overall switching history. Here, we present a systematic Kelvin probe microscopy study of the effect of relative humidity and polarisation switching history on the surface charge dissipation in ferroelectric Pb(Zr0.2Ti0.8)O3 thin films. We analyse the interaction of surface charges with ferroelectric domains through the framework of physically constrained unsupervised machine learning matrix factorisation, Dictionary Learning, and reveal a complex interplay of voltage-mediated physical processes underlying the observed signal decays. Additional insight into the observed behaviours is given by a Fitzhugh–Nagumo reaction–diffusion model, highlighting the lateral spread and charge passivation process contributors within the Dictionary Learning analysis., The authors acknowledge Dr Sergei V. Kalinin of Oak Ridge National Laboratory, for helpful discussions about machine learning and the initial suggestion to explore reaction–diffusion modelling. This work was supported by Division II of the Swiss National Science Foundation under project 200021_178782. A.V. acknowledges support by the Spanish Government under the project PID2019-110907GB-I00 and the “Severo Ochoa” Program for Centres of Excellence in R&D (CEX2019-000917-S). N.D. acknowledges support by the Spanish Government under the project PID2019- 109931GB-I00. N.B.G. acknowledges support by the National Science Foundation under the project DMR-20269676. The authors would like to thank S. Muller for technical support., With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000917-S).

Research in materials science increasingly depends on the correlation of information from multiple characterisation techniques, acquired in ever larger datasets. Efficient methods of processing and storing these complex datasets are therefore crucial. Reliably keeping track of data processing is also essential to conform with the goals of open science. Here, we introduce Hystorian, a generic materials science data analysis Python package built at its core to improve the traceability, reproducibility, and archival ability of data processing. Proprietary data formats are converted into open hierarchical data format (HDF5) files, with both datasets and subsequent workflows automatically stored into a single location, thus allowing easy management of multiple data types. At present, Hystorian provides a basic scanning probe microscopy and x-ray diffraction analysis toolkit, and is readily extensible to suit user needs. It is also able to wrap over any existing processing functions, making it easy to append in an extant workflow.

From the survival of biological systems to the surface electrochemistry of materials, ambient and surface water plays a key role, and understanding its contribution is essential to describe the observed behaviours fully. Thus, precise measurement and control of relative humidity are essential in laboratory applications. In this paper, we present an open source hardware and software design of a low-noise and high precision standalone relative humidity controller, whose versatility and potential applications we demonstrate through basic calibration and performance tests. The hardware and software implementation are discussed in detail, with all plans and codes provided.