Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …

Extracting structured knowledge from scientific text remains a challenging task for machine
learning models. Here, we present a simple approach to joint named entity recognition and …

Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

A large amount of materials science knowledge is generated and stored as text published in
peer-reviewed scientific literature. While recent developments in natural language …

The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …

Abstract “Artificial Intelligence” in all its forms has emerged as a transformative technology
that is in the process of reshaping many aspects of industry and wider society at a global …

Abstract The Doyle–Fuller–Newman (DFN) framework is the most popular physics-based
continuum-level description of the chemical and dynamical internal processes within …

A great number of scientific papers are published every year in the field of battery research,
which forms a huge textual data source. However, it is difficult to explore and retrieve useful …

Research publications are the major repository of scientific knowledge. However, their
unstructured and highly heterogenous format creates a significant obstacle to large-scale …