Over the past few decades, intensive research is performed to fulfil the future requirements for low-cost energy conversion and storage. Functional materials have replaced existing materials in many applications impacting every part of our lives and they have become ubiquitous. In this context, relevant fields are addressed such as photovoltaics, batteries, fuel cells, super-capacitors, and emerging energy harvesting devices based on thermoelectric and piezoelectric effects.

Since nanoscale phenomena at surfaces and interfaces play an essential role in energy conversion and energy storage, our talk will focus on the last cutting-edge developments of scanning probe microscopies (SPM) for the characterization of the electrical properties of materials to understand the behaviour of modern energy conversion and energy storage materials (including in operando).

Beyond surface imaging, we will highlight the abilities of SPM to characterize the electrical and piezoelectric properties of materials with techniques such as (photo)conducting AFM, Kelvin Probe Force Microcopy, Piezoresponse Force Microscopy, and Scanning Microwave Impedance Microscopy for applications in hybrid photovoltaics and piezoelectric energy harvesting applications.

In this context, Machine Learning techniques are now mature to analyse the data ideally user-independent and have been perceived as a promising tool for the design and discovery of novel materials for a broad range of applications. We will shortly discuss computational methods and ML algorithms dealing with data clustering (such as K-Means or Automatic Gaussian Mixture Model) that can be used to detect the different domains and (inter)phases in materials by partitioning the recorded data (i.e. the observables) into clusters according to their similarities.

For instance, we proposed adapted protocols for the data analysis, expecting to help the scientific community to better understand the key parameters in the optimization of the behaviour of materials not only for fundamental aspects but also for industrial applications. This algorithmically driven approach will enable analyze materials with more complex architectures and/or other properties, opening new avenues of research on advanced materials with specific functions and desired properties leading to the creation of functional, more reliable and ideally eco-responsible materials.