The electrochemical sensor (E-sensors) market trends have identified the biomedical
applications as a significant market growth with impact on personalized therapy. Given the …

Adsorbates often cover the surfaces of catalysts densely as they carry out reactions,
dynamically altering their structure and reactivity. Understanding adsorbate-induced …

The observation of superconductivity in hydride-based materials under ultrahigh pressures
(for example, H 3 S and LaH 10) has fueled the interest in a more data-driven approach to …

Abstract Structure-informed materials informatics is a rapidly evolving discipline of materials
science relying on the featurization of atomic structures or configurations to construct vector …

Computational materials science produces large quantities of data, both in terms of high-
throughput calculations and individual studies. Extracting knowledge from this large and …

Interactive notebooks are a precious tool for creating graphical user interfaces and teaching
materials. Python and Jupyter are becoming increasingly popular in this context, with …

Combinatorial and guided screening of materials space with density-functional theory and
related approaches has provided a wealth of hypothetical inorganic materials, which are …

nimCSO is a high-performance tool implementing several methods for selecting components
(data dimensions) in compositional datasets, which optimize the data availability and density …

In materials science, machine learning (ML) has become an essential and indispensable
tool. ML has emerged as a powerful tool in materials science, particularly for predicting …

Two key issues hindering the transition towards FAIR data science are the poor
discoverability and inconsistent instructions for the use of data extractor tools, ie, how we go …