Beyond bulk single crystals: A data format for all materials structure–property–processing relationships
In this article, we discuss a hierarchical data structure used for storing materials data called the physical information file (PIF).
Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns
Manual attribution of crystallographic phases from high-throughput x-ray diffraction studies is an arduous task, and represents a rate-limiting step in high-throughput exploration of new materials. Here, we demonstrate a semi-supervised machine learning technique, SS-AutoPhase, which uses a two-step approach to identify automatically phases from diffraction data. First, clustering analysis is used to select a representative […]
The Materials Data Facility: Data Services to Advance Materials Science Research
With increasingly strict data management requirements from funding agencies and institutions, expanding focus on the challenges of research replicability, and growing data sizes and heterogeneity, new data needs are emerging in the materials community. The materials data facility (MDF) operates two cloud-hosted services, data publication and data discovery, with features to promote open data sharing, […]
Materials Data Infrastructure: A Case Study of the Citrination Platform to Examine Data Import, Storage, and Access
Considerations are presented around the design of a materials data infrastructure including import of structured and unstructured data, storage of that data for archival and retrieval, and access to that data through programmatic and graphical interfaces. In particular, the choices around technologies used in such an infrastructure, the benefits and drawbacks of those technologies, and […]
Web-based machine learning models for real-time screening of thermoelectric materials properties
The experimental search for new thermoelectric materials remains largely confined to a limited set of successful chemical and structuralfamilies, such as chalcogenides, skutterudites, and Zintl phases. In principle, computational tools such as density functional theory (DFT)offer the possibility of rationally guiding experimental synthesis efforts toward very different chemistries. However, in practice, predicting thermoelectric properties from first principles remains a challenging endeavor [J. […]
Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties
The experimental search for new thermoelectric materials remains largely confined to a limited set of successful chemical and structural families, such as chalcogenides, skutterudites, and Zintl phases. In principle, computational tools such as density functional theory (DFT) offer the possibility of rationally guiding experimental synthesis efforts toward very different chemistries. However, in practice, predicting thermoelectric properties from first principles remains a challenging endeavor [J. […]
Materials science with large-scale data and informatics: Unlocking new opportunities
Universal access to abundant scientific data, and the software to analyze the data at scale, could fundamentally transform the field of materials science. Today, the materials community faces serious challenges to bringing about this data-accelerated research paradigm, including diversity of research areas within materials, lack of data standards, and missing incentives for sharing, among others. […]
New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships
However, most materials property information remains scattered across multiple resources […] We note that Citrine Informatics (http://www.citrine.io) is one commercial entity that is attempting to centralize information collected from diverse sources (both experimental and computational). Such analyses are culminating in more general “recommender” systems that can suggest new compounds based on observed data. For example, Citrine Informatics has built […]
Materials informatics across the product lifecycle: Selection, manufacturing, and certification
The process of taking a new material from invention to deployment can take 20 years or more. Since the announcement of the Materials Genome Initiative in 2011, new attention has been paid to accelerating this timeframe to address key challenges in industries from energy, to biomedical materials, to catalysis, to polymers, particularly in the development of new materials discovery techniques. Materials informatics, or algorithmically analyzing materials data at scale to […]
Perspective: Materials informatics across the product lifecycle: Selection, manufacturing, and certification
The process of taking a new material from invention to deployment can take 20 years or more. Since the announcement of the Materials Genome Initiative in 2011, new attention has been paid to accelerating this timeframe to address key challenges in industries from energy, to biomedical materials, to catalysis, to polymers, particularly in the development of new materials discovery techniques. Materials informatics, or algorithmically analyzing materials data at scale to gain […]