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,…
Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum. Often, an expert operator must use manual trial-and-error — possibly making thousands of prints — to determine…