Perspective: Data infrastructure for high throughput materials discovery
Computational capability has enabled materials design to evolve from trial-and-error towards more informed methodologies that require large amounts of data. Expert-designed tools and their underlying databases facilitate modern-day high throughput computational methods. Standard data formats and com...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2016-05-01
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| Series: | APL Materials |
| Online Access: | http://dx.doi.org/10.1063/1.4942634 |
| Summary: | Computational capability has enabled materials design to evolve from trial-and-error towards more informed methodologies that require large amounts of data. Expert-designed tools and their underlying databases facilitate modern-day high throughput computational methods. Standard data formats and communication standards increase the impact of traditional data, and applying these technologies to a high throughput experimental design provides dense, targeted materials data that are valuable for material discovery. Integrated computational materials engineering requires both experimentally and computationally derived data. Harvesting these comprehensively requires different methods of varying degrees of automation to accommodate variety and volume. Issues of data quality persist independent of type. |
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| ISSN: | 2166-532X |