Machine Learning applications to Materials Science
- 0 Collaborators
In recent years, materials science research has produced many data from simulations of new materials. However, the simulation process is often computational costly. Machine learning is being applied to these data to build predictive models that can replace simulation, with a fraction of the costs. ...learn more
Project status: Under Development
Intel Technologies
oneAPI
Overview / Usage
To develop machine learning algorithms for materials science prediction.
Methodology / Approach
Use estate-of-the-art machine learning techniques on public available materials science data.
Technologies Used
scikit-learn
pytorch
keras
Other links
- Ab initio insights into the structural, energetic, electronic, and stability properties of mixed Ce n Zr 15− n O 30 nanoclusters
- Machine learning prediction of nine molecular properties based on the SMILES representation of the QM9 quantum-chemistry dataset
- Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition
- Correlation-Based Framework for Extraction of Insights from Quantum Chemistry Databases: Applications for Nanoclusters