From "Dark Data" to Smart Labs: A Roadmap for Reliable AI in Materials Science
Chinese Academy of Sciences
The shift from slow, trial-and-error experimentation to data-driven discovery is reshaping how we develop energy materials. A new Perspective argues that the design of materials databases--how they ingest, curate and share information--directly determines the trustworthiness of modern artificial intelligence (AI) models.