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  • Writer's pictureAnjaneya Turai

Machine Learning to Predict the Electronic Band Structure of Materials

Artificial intelligence (AI) and machine learning have the potential to revolutionize the field of materials science. In recent years, researchers have been exploring the use of these techniques to predict the electronic band structure of materials from band mapping data.

The electronic band structure of a material is a fundamental property that determines its electrical and optical properties. It describes the range of energy levels that electrons in the material can occupy and how these energy levels are distributed. Traditionally, the electronic band structure of a material has been determined through experiments, such as angle-resolved photoemission spectroscopy (ARPES). However, these experiments can be time-consuming and expensive, and may not be feasible for all materials.


To overcome these limitations, researchers have turned to machine learning and AI techniques to predict the electronic band structure of materials from band mapping data. These techniques use algorithms that are trained on a large dataset of materials and their corresponding electronic band structures. The algorithms are then able to predict the electronic band structure of a new material based on its band mapping data. In their proposed method, the authors utilize a probabilistic machine learning model to fit a model to the band mapping data, with the goal of accurately predicting the energy values of the material's electronic band structure. The model utilizes a nearest-neighbor Gaussian distribution to describe the proximity of energy values at nearby momenta. To find the optimal fit to the data, the authors utilize maximum a posteriori estimation in probabilistic inference.

One unique aspect of this approach is its ability to incorporate imperfect physical knowledge, such as the presence of impurities or defects in the material, as well as handle noise in the data. This makes the model more robust and able to handle real-world scenarios where the data may not be perfect. Overall, the use of this probabilistic machine learning model allows for a more accurate prediction of the electronic band structure of materials from band mapping data.


One approach that has been successful in predicting the electronic band structure of materials is called machine learning-based band structure prediction (MLBSP). In MLBSP, the algorithm is trained on a dataset of materials and their corresponding electronic band structures. The algorithm is then able to predict the electronic band structure of a new material based on its band mapping data.


Another approach is called deep learning-based band structure prediction (DLBSP). In DLBSP, the algorithm is trained on a dataset of materials and their corresponding electronic band structures using a deep neural network. The neural network is able to learn the complex relationships between the band mapping data and the electronic band structure of the material.


Overall, the use of machine learning and AI techniques to predict the electronic band structure of materials from band mapping data has the potential to greatly accelerate the discovery and development of new materials. It may also enable the study of materials that are difficult or impossible to examine using traditional experimental techniques. To know more about this research, you can read this paper - Click Me!


- Anjaneya Krishna Turai

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