Data driven or machine learning (ML) based methods have been recently used in materials
science to provide quick material property predictions. Although powerful and robust, these …

Emerging computation-and data-driven approaches are particularly useful for rationally
designing materials with targeted properties. Generally, these approaches rely on identifying …

The recent successes of the Materials Genome Initiative have opened up new opportunities
for data-centric informatics approaches in several subfields of materials research, including …

Polymers are incredibly versatile materials and have become ubiquitous. Increasingly,
researchers are using data science and polymer informatics to design new materials and …

The design of new functional polymers depends on the successful navigation of their
structure-function landscapes. Advances in combinatorial polymer chemistry and machine …

Machine learning has the potential to dramatically accelerate high-throughput approaches
to materials design, as demonstrated by successes in biomolecular design and hard …

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

Artificial intelligence (AI) based approaches are beginning to impact several domains of
human life, science and technology. Polymer informatics is one such domain where AI and …

Polymers are an important class of materials with vast arrays of physical and chemical
properties and have been widely used in many applications and industrial products …

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 …