Accelerating Materials Discovery: Combining Machine Learning and First Principles Calculations

Machine learning and first principles calculations can be combined to enhance the accuracy and efficiency of materials design and discovery. First principles calculations involve solving the Schr￶dinger equation to determine the electronic properties of materials. This approach provides a fundamental understanding of the properties of materials but can be computationally expensive and time-consuming.

Machine learning, on the other hand, involves training algorithms on large datasets to recognize patterns and make predictions. By combining these two approaches, researchers can use machine learning algorithms to predict the properties of materials based on the results of first principles calculations. This approach can significantly reduce the computational cost of materials design and discovery while maintaining high accuracy.

For example, machine learning algorithms can be trained on the results of first principles calculations to predict the properties of new materials, such as their electronic band structure, thermodynamic stability, and mechanical properties. This approach can be used to identify new materials with desirable properties for specific applications, such as energy storage or catalysis.

In summary, the combination of machine learning and first principles calculations can accelerate materials design and discovery by reducing the computational cost and improving the accuracy of predictions.