AI Approach Accelerates Speed of Predicting the Thermal Properties of Materials

Estimates show that roughly 70% of energy generated around the globe is wasted. Experts believe that more efficient systems to generate power could be designed if researchers were able to forecast how heat passes through insulators and semiconductors. This is hard to do at the moment, however, because it is challenging to model the thermal properties of different materials.

The issue stems from subatomic particles that carry heat, known as phonons. A group of MIT researchers have designed a new framework based on machine-learning, which can forecast phonon dispersion relation about 1,000 times faster than other methods based on artificial intelligence (“AI”).

This technique may assist engineers in designing systems to generate energy that efficiently generate more power. It could also aid in the development of efficient microelectronics.

Mingda Li, an associate professor of engineering and nuclear science, states that obtaining the properties of phonons is challenging, either experimentally or computationally. The researchers’ approach calculates the phonon dispersion relation for a material, with the researchers using graph neural networks to convert the atomic structure of a material into a graph made up of multiple nodes linked by edges.

While graph neural networks can help calculate quantities such as electrical polarization or magnetization, they aren’t flexible in a way that allows them to forecast phonon dispersion relation. To acquire flexibility, the researchers came up with a virtual node graph neural network that allows the neural network’s output to vary. This allowed them to estimate phonon dispersion relations, with the researchers observing that the virtual node graph neural network provided more accuracy when forecasting the heat capacity of a material.

The researchers believe that this efficiency may enable the creation of a bigger space when looking for materials with specific thermal properties such as superconductivity, or thermal storage. Additionally, they posit that their technique could also be utilized in forecasting challenging magnetic and optical properties.

For the future, the scientists are focused on refining this method so virtual nodes can record any small change that may impact the structure of phonons with additional sensitivity.

Li, the senior author of this study, was joined in doing this research by MIT’s Thomas Siebel; professor of electrical engineering and computer science Tommi Jaakkola; chemistry grad student Ryotaro Okabe; and MIT electrical engineering and computer science graduate student Abhijatmedhi Chotrattanapituk, among others.

Their findings were reported in “Nature Computational Science.”

This study was supported by the Oak Ridge National Laboratory, the Harvard Quantum Initiative, a Sow-Hsin Chen Fellowship, a Mathworks Fellowship, the National Science Foundation and the U.S. Department of Energy.

It is anticipated that during the coming years, tech companies such as Intel Corp. (NASDAQ: INTC) are likely to bring many innovative AI solutions onto the market that could revolutionize the different industries in which they are used.

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