Machine learning (ML) has become a part of the fabric of high-throughput screening and
computational discovery of materials. Despite its increasingly central role, challenges …

We are excited to present this Special Topic collection on Machine Learning for Materials
Design and Discovery in the Journal of Applied Physics. With a wide range of exciting and …

The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …

Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …

Conspectus Designing new materials is vital for addressing pressing societal challenges in
health, energy, and sustainability. The combination of physicochemical laws and empirical …

Machine Learning for Materials Science provides the fundamentals and useful insight into
where Machine Learning (ML) will have the greatest impact for the materials science …

Materials development has historically been driven by human needs and desires, and this is
likely to continue in the foreseeable future. The global population is expected to reach ten …

Machine learning, applied to chemical and materials data, is transforming the field of
materials discovery and design, yet significant work is still required to fully take advantage of …

Developing algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …

The exponential growth and success of machine learning (ML) has resulted in its application
in all scientific domains including material science. Advancement in experimental …