Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

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 …

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—
has played a part in science as far back as the 18th century when Laplace used it to guide …

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic
developments and the resounding successes of data-driven efforts in other domains …

The screening of novel materials with good performance and the modelling of quantitative
structure-activity relationships (QSARs), among other issues, are hot topics in the field of …

One of the main challenges in materials discovery is efficiently exploring the vast search
space for targeted properties as approaches that rely on trial-and-error are impractical. We …

Conventional machine learning approaches for predicting material properties from
elemental compositions have emphasized the importance of leveraging domain knowledge …

In metals additive manufacturing (AM), materials and components are concurrently made in
a single process as layers of metal are fabricated on top of each other in the near-final …

The current predictive modeling techniques applied to Density Functional Theory (DFT)
computations have helped accelerate the process of materials discovery by providing …

Our ability to collect “big data” has greatly surpassed our capability to analyze it,
underscoring the emergence of the fourth paradigm of science, which is datadriven …