Artificial intelligence aids materials fabrication
In recent years, research efforts such as the Materials Genome Initiative and the Materials Project have produced a wealth of computational tools for designing new materials useful for a range of applications, from energy and electronics to aeronautics and civil engineering.
But developing processes for producing those materials has continued to depend on a combination of experience, intuition, and manual literature reviews.
A team of researchers at MIT, the University of Massachusetts at Amherst, and the University of California at Berkeley hope to close that materials-science automation gap, with a new artificial-intelligence system that would pore through research papers to deduce “recipes” for producing particular materials.
“Computational materials scientists have made a lot of progress in the ‘what’ to make — what material to design based on desired properties,” says Elsa Olivetti, the Atlantic Richfield Assistant Professor of Energy Studies in MIT’s Department of Materials Science and Engineering (DMSE). “But because of that success, the bottleneck has shifted to, ‘Okay, now how do I make it?’”
The researchers envision a database that contains materials recipes extracted from millions of papers. Scientists and engineers could enter the name of a target material and any other criteria — precursor materials, reaction conditions, fabrication processes — and pull up suggested recipes.
As a step toward realizing that vision, Olivetti and her colleagues have developed a machine-learning system that can analyze a research paper, deduce which of its paragraphs contain materials recipes, and classify the words in those paragraphs according to their roles within the recipes: names of target materials, numeric quantities, names of pieces of equipment, operating conditions, descriptive adjectives, and the like.
In a paper appearing in the latest issue of the journal Chemistry of Materials, they also demonstrate that a machine-learning system can analyze the extracted data to infer general characteristics of classes of materials — such as the different temperature ranges that their synthesis requires — or particular characteristics of individual materials — such as the different physical forms they will take when their fabrication conditions vary.
Olivetti is the senior author on the paper, and she’s joined by Edward Kim, an MIT graduate student in DMSE; Kevin Huang, a DMSE postdoc; Adam Saunders and Andrew McCallum, computer scientists at UMass Amherst; and Gerbrand Ceder, a Chancellor’s Professor in the Department of Materials Science and Engineering at Berkeley.
The researchers trained their system using a combination of supervised and unsupervised machine-learning techniques. “Supervised” means that the training data fed to the system is first annotated by humans; the system tries to find correlations between the raw data and the annotations.
