Publications

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 […]

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, […]

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. […]

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. […]

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 […]

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 data and model repositories include those at NIST, 66 Citrine Informatics’ system, 67 NanoHuB, 68 and the National Data Service’s Materials Data Facility. McDowell, D. L., & Kalidindi, S. R. (2016). The materials innovation ecosystem: A key enabler for the Materials Genome Initiative. MRS Bulletin, 41(04), 326-337.

We present an overview and preliminary analysis of computed thermoelectric properties for more than 48 000 inorganic compounds from the Materials Project (MP). We compare our calculations with available experimental data to evaluate the accuracy of different approximations in predicting thermoelectric properties. We observe fair agreement between experiment and computation for the maximum Seebeck coefficient determined […]

Some of these challenges have solutions on the horizon of which the authors are aware of. For example, Citrine Informatics (citrination.com) has undertaken the task of developing an open access database that will provide data infrastructure for all materials properties, both calculated and measured experimentally. Seshadri, R., & Sparks, T. D. (2016). Perspective: Interactive material property databases through […]

One organization that has made significant progress in establishing a centralized data resource for materials scientists is Citrine Informatics, a company that specializes in applying data mining to materials discovery and optimization. Jain, A., Persson, K. A., & Ceder, G. (2016). Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases. […]