A lossless data management platform for machine learning and sharing of experimental information
In the field of materials science, even small variations in experimental parameters and protocols can lead to unwanted changes in the properties of a material. A ground-breaking development in this field came with the advent of materials informatics—a heavily data-reliant field, which focuses on materials data, including synthesis protocols, properties, mechanisms, and structures. It has benefitted significantly from artificial intelligence (AI), which enables large-scale, automated data-analyses, material design, and experiments which can aid the discovery of useful materials.
Researchers at the Indian Institute of Science (IISc), with collaborators at University College London, have developed machine learning-based methods to predict material properties even with limited data. This can aid in the discovery of materials with desired properties, such as semiconductors.
Developing new materials requires significant time and labor, but some chemists are now hopeful that artificial intelligence (AI) could one day shoulder much of this burden. In a new study, a team prompted a popular AI model, ChatGPT, to perform one particularly time-consuming task: searching scientific literature. With that data,…
Nanoengineers at the University of California San Diego's Jacobs School of Engineering have developed an AI algorithm that predicts the structure and dynamic properties of any material—whether existing or new—almost instantaneously. Known as M3GNet, the algorithm was used to develop matterverse.ai, a database of more than 31 million yet-to-be-synthesized materials…