Scientists at the University of North Carolina (UNC) at Chapel Hill and Duke University have created the first general-purpose method for using machine learning to predict the properties of new metals, ceramics and other crystalline materials, and to find new uses for existing materials. This advance could greatly speed up the current trial-and-error process of creating new and better materials.
Researchers led by Olexandr Isayev and Alexander Tropsha the UNC Eshelman School of Pharmacy used data on approximately 60,000 unique materials from the US National Institute of Standards and Technology's Inorganic Crystal Structure Database to create a new methodology they call Properties Labeled Materials Fragments (PLMF).
Using machine learning to analyze and model existing crystal structures, the PLMF method is able to predict the properties of new materials proposed by scientists and engineers. The tool was even able to fill in missing values for properties of materials in the NIST database that had never been tested experimentally.
"Technology is often driven by the discovery of new materials, but the process of discovering these materials has always been rather haphazard," Tropsha said. "Our new tool applies the data- and knowledge-driven approach we use in the pharmaceutical sciences to design drugs. Because creating new materials takes an incredible amount of time and effort that often ends in disappointment, our PLMF tool allows materials scientists to test a new idea before they even lift a finger to synthesize it."
Tropsha is a professor in the school of pharmacy and director of the Laboratory for Molecular Modeling, while Isayev is a research assistant professor. Their work is published in a paper in Nature Communications, and the PLMF tool is publicly available as a user-friendly web application at http://alfow.org/aflow-ml.
"We can leverage what we know about these materials and savvy machine learning to rapidly screen potential materials for the right property. Researchers can quickly narrow candidate materials and avoid many extraneous and complex calculations. This saves money, time and computational resources."Olexandr Isayev, University of North Carolina at Chapel Hill
The PLMF method works by creating ‘fingerprints’ from the structure of the crystals comprising the smallest units of inorganic materials like ceramics, metals and metal alloys. Combining the fingerprints with machine learning allowed the scientists to create universal models capable of accurately predicting eight critical electronic and thermomechanical properties of virtually any inorganic crystalline material. These properties include conductivity, stiffness and compressibility, heat transfer and response to temperature change, and the team plans to incorporate more properties as they collect more data.
"In many practical projects, people know the range of values they want for a particular property," Isayev said. "We can leverage what we know about these materials and savvy machine learning to rapidly screen potential materials for the right property. Researchers can quickly narrow candidate materials and avoid many extraneous and complex calculations. This saves money, time and computational resources."
In the first practical application of the PLMF method, the team worked with Jim Cahoon in the UNC Department of Chemistry to design a new electrode material for a type of low-cost solar cell. Nickel oxide is the current electrode material, but it’s not very efficient, toxic and requires organic solvents to work in the cell.
Scientists virtually screened 50,000 known inorganic compounds and identified lead titanate as the most promising material, which was confirmed by subsequent testing. Devices using lead titanate exhibited the best performance in aqueous solution, allowing the organic solvents to be replaced by a water-based solution that could help drive down costs while being more environmentally friendly.
"Lead titanate likely would not have been the first choice of most materials scientists because its structure is so dissimilar to nickel oxide," Isayev said. "Materials derived from iron, cobalt or copper would be more likely to be considered because they are more chemically similar to nickel. The PLMF and machine learning found a simple and novel solutions that saved untold hours of trial-and-error searching."
This story is adapted from material from the University of North Carolina at Chapel Hill, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.