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 of new materials discovery techniques. Materials informatics, or algorithmically analyzing materials data at scale to […]
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 […]
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 […]
In this article we provide an overview of data mining, informatics, and machine learning approaches for thermoelectrics. We describe how the initial development of a thermoelectric materials database has enabled the creation of a recommendation engine governed by machine learning and how this engine introduces a new paradigm in thermoelectric materials development. Performance probability is generated based on […]
