Journal:An open experimental database for exploring inorganic materials
| Full article title | An open experimental database for exploring inorganic materials |
|---|---|
| Journal | Scientific Data |
| Author(s) | Zakutayev, Andriy; Wunder, Nick; Schwarting, Marcus; Perkins, John D.; White, Robert; Munch, Kristin; Tumas, William; Phillips, Caleb |
| Author affiliation(s) | National Renewable Energy Laboratory |
| Primary contact | Email: See original article |
| Year published | 2018 |
| Volume and issue | 5 |
| Page(s) | 180053 |
| DOI | 10.1038/sdata.2018.53 |
| ISSN | 2052-4463 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.nature.com/articles/sdata201853 |
| Download | https://www.nature.com/articles/sdata201853.pdf (PDF) |
 |
This article should not be considered complete until this message box has been removed. This is a work in progress. |
Abstract
The use of advanced machine learning algorithms in experimental materials science is limited by the lack of sufficiently large and diverse datasets amenable to data mining. If publicly open, such data resources would also enable materials research by scientists without access to expensive experimental equipment. Here, we report on our progress towards a publicly open High Throughput Experimental Materials (HTEM) Database (htem.nrel.gov). This database currently contains 140,000 sample entries, characterized by structural (100,000), synthetic (80,000), chemical (70,000), and optoelectronic (50,000) properties of inorganic thin film materials, grouped in >4,000 sample entries across >100 materials systems; more than a half of these data are publicly available. This article shows how the HTEM database may enable scientists to explore materials by browsing web-based user interface and an application programming interface. This paper also describes a HTE approach to generating materials data and discusses the laboratory information management system (LIMS) that underpins the HTEM database. Finally, this manuscript illustrates how advanced machine learning algorithms can be adopted to materials science problems using this open data resource.
Keywords: applied physics, electronic devices, materials chemistry, semiconductors, solar cells
Introduction
Machine learning is a branch of computer science concerned with algorithms that can develop models from the available data, reveal trends and correlation in this data, and make predictions about unavailable data. The predictions rely on data mining, the process of discovering patterns in large data sets using statistical methods. Machine learning methods have been recently successful in process automation, natural language processing, and computer vision, where large databases are available to support data-driven modeling efforts. These successes also sparked discussions about the potential of artificial intelligence in science[1] and the Fourth Paradigm[2] of data-driven scientific discovery. In materials science, applying artificial intelligence to data-driven materials discovery is important, because new materials often underpin major advances in modern technologies. For example, in advanced energy technologies, efficient solid state lighting was enabled by the use of gallium nitride in light-emitting diodes, electric cars were brought to life by intercalation materials used in lithium-ion batteries, and modern computers would not have been possible without the material silicon.
References
- ↑ "The AI revolution in science". Science. 7 July 2017. doi:10.1126/science.aan7064. http://www.sciencemag.org/news/2017/07/ai-revolution-science.
- ↑ Hey, T.; Tansley, S.; Tolle, K. (2009). The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research. ISBN 9780982544204. https://www.microsoft.com/en-us/research/publication/fourth-paradigm-data-intensive-scientific-discovery/.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.
