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Discovering New Materials Knowledge from 'Old Data'
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PRE
AI
DOI：10.1039/D6CC01716A.png)
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Abstract
En 中文
From 'old data' to new knowledge discovery; this paradigm is fundamentally reshaping research in chemistry and materials. Unlike traditional trial-and-error approaches; knowledge mining driven by large-scale databases offers unprecedented potential in exploring complex compositional spaces and accelerating rational materials design. In thie review; we highlight three significant progresses in discovering new materials knowledge from 'old literature data': (1) In the field of catalysis; data-driven approaches reveal new phenomena and limitations of existing theoretical models; greatly accelerating materials design and screening. (2) In the field of solid-state electrolytes; data empowerment accelerates the understanding of underlying physical mechanisms. (3) In the field of hydrogen storage; we demonstrate a pathway from 'old data' to structured knowledge and finally to autonomous design. Finally; we highlight the critical role of database construction in data intelligence and the development of AI agents for materials design. Looking ahead; such data-driven models will continue to deepen our knowledge generation and accelerate the discovery of target materials in the relevant field. By integrating knowledge generation from 'old data'; theoretical simulations; and experimental validation; this approach promises to establish a digital materials ecosystem for cross-disciplinary innovation; where materials discovery will be continuously accelerated.
Keywords:
data mining
materials discovery
catalysis
solid-state electrolytes
hydrogen storage