Year: 2019
Type: data-paper
Abstract: Materials discovery has become significantly facilitated and accelerated by high-throughput ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of "codified recipes" for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis.
Source: Scientific Data
Authors: Olga Kononova, Haoyan Huo, Tanjin He, Ziqin Rong, Tiago Botari, Wenhao Sun, Vahe Tshitoyan, Gerbrand Ceder
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,690
Awards: G1183917127, G1286236842, G1369386836, DMREF: Collaborative Research: The Synthesis Genome: Data Mining for Synthesis of New Materials, SPARSE, FAST REAL SPACE METHODS FOR THE PARTIAL DIFFERENTIAL EQUATIONS ARISING IN ELECTRONIC MATERIALS DESIGN, G3083819904, G3255612512, G42916606, G5261056217, G8876996369, G969889393
OpenAlex ID: W2980932864