Year: 2024
Type: preprint
Abstract: Machine Learning (ML) is accelerating the progress of materials prediction and classification, with particular success in CGNN designs. While classical ML methods remain accessible, advanced deep networks are still challenging to build and train. We introduce two new adaptations and refine two existing ML networks for generic crystalline quantum materials properties prediction and optimization. These new models achieve state-of-the-art performance in predicting TQC classification and strong performance in predicting band gaps, magnetic classifications, formation energies, and symmetry group. All networks easily generalize to all quantum crystalline materials property predictions. To support this, full implementations and automated methods for data handling and materials predictions are provided, facilitating the use of deep ML methods in quantum materials science. Finally, dataset error rates are analyzed using an ensemble model to identify and highlight highly atypical materials for further investigations.
Source: arXiv (Cornell University)
Authors: Nop, Gavin, Mundy, Micah, Paudyal, Durga, Smith, Jonathan
Institutions: Iowa State University, University of Iowa
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4402963190