New machine learning model quickly and accurately predicts dielectric function – Phys.org

A team of researchers from the University of Washington has developed a machine learning model that can quickly and accurately predict the dielectric function of materials. The dielectric function is a key property that determines how a material responds to electromagnetic radiation, and it is used in a wide range of applications, including the design of solar cells, LEDs, and other optical devices.

The new model is based on a deep neural network, a type of machine learning algorithm that can learn complex patterns from data. The researchers trained the model on a dataset of over 100,000 materials, and they found that it was able to predict the dielectric function of new materials with high accuracy.

“This is a major breakthrough in materials science,” said Dr. Xiaoxing Xu, the lead author of the study. “Our model can predict the dielectric function of materials much faster and more accurately than traditional methods, which can take weeks or even months. This will significantly accelerate the discovery and development of new materials with desired optical properties.”

The researchers believe that their model will have a major impact on the fields of materials science, photonics, and optics. It could lead to the development of new and more efficient solar cells, LEDs, and other optical devices.

The dielectric function is a complex quantity that describes the response of a material to an external electric field. It is a key property that determines the optical properties of materials, such as their reflectivity, absorption, and transmission of light.

Traditionally, the dielectric function has been calculated using time-consuming and computationally intensive quantum mechanical calculations. These methods can take weeks or even months to complete, and they are often limited to simple materials with relatively few atoms. The new machine learning model offers a much faster and more efficient way to predict the dielectric function, and it can be applied to a wider range of materials, including complex materials with thousands of atoms.

The researchers trained their model on a dataset of over 100,000 materials, including metals, semiconductors, insulators, and organic materials. They used a deep neural network architecture with multiple layers of neurons, which allows the model to learn complex patterns from the data.

“Our model was able to accurately predict the dielectric function of materials that were not included in the training set,” said Xu. “This suggests that our model has learned a general understanding of how the dielectric function is related to the material’s composition and structure.”

The researchers believe that their model will be valuable for a variety of applications, including:

• Accelerated discovery and development of new materials with desired optical properties
• Design of new and more efficient solar cells, LEDs, and other optical devices
• Optimization of existing materials for specific applications
• Predicting the optical properties of new and complex materials

The researchers are now working to further improve the model’s accuracy and to extend it to other properties of materials, such as their thermal and electrical properties.

“Our work demonstrates the power of machine learning for accelerating materials science research,” said Xu. “We believe that this technology has the potential to revolutionize the way we design and develop new materials.”