Organic molecules and polymers have a broad range of applications in biomedical,
chemical, and materials science fields. Traditional design approaches for organic molecules …

In recent years, there has been a large effort in the materials science community to employ
materials informatics to accelerate materials discovery or to develop new understanding of …

Sequential learning (SL) strategies, ie iteratively updating a machine learning model to
guide experiments, have been proposed to significantly accelerate materials discovery and …

The martensitic transformation serves as the basis for applications of shape memory alloys
(SMAs). The ability to make rapid and accurate predictions of the transformation temperature …

In recent years materials informatics, which is the application of data science to problems in
materials science and engineering, has emerged as a powerful tool for materials discovery …

Bayesian optimization (BO) has recently emerged as a powerful and flexible tool for hyper-
parameter tuning and more generally for the efficient global optimization of expensive black …

Guiding experiments to find materials with targeted properties is a crucial aspect of materials
discovery and design, and typically multiple properties, which often compete, are involved …

Advanced materials with improved properties have the potential to fuel future technological
advancements. However, identification and discovery of these optimal materials for a …

Modern scientific instruments are acquiring data at ever-increasing rates, leading to an
exponential increase in the size of data sets. Taking full advantage of these acquisition rates …

Materials informatics employs techniques, tools, and theories drawn from the emerging
fields of data science, internet, computer science and engineering, and digital technologies …