Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …

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 …

As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

There has been a growing effort to replace manual extraction of data from research papers
with automated data extraction based on natural language processing, language models …

A prominent technique for self-supervised representation learning has been to contrast
semantically similar and dissimilar pairs of samples. Without access to labels, dissimilar …

Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …

Skin-interfaced electronics is gradually changing medical practices by enabling continuous
and non-invasive tracking of physiological and biochemical information. With the rise of big …

The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …

Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …

A bottleneck in efficiently connecting new materials discoveries to established literature has
arisen due to an increase in publications. This problem may be addressed by using named …