Undergraduate junior student Daniel Gleaves published his research on deep learning models for new materials discovery

Our computer science junior student Daniel Gleaves from Prof. Jianjun Hu’s group published his research of deep learning algorithms for materials research in Digital Discovery Journal from Royal Society of Chemistry. In this work, he applied semi-supervised deep graph neural networks for material synthesizability and stability prediction. His models can achieve significantly better performance compared to the existing state-of-the-art PU learning methods with the true positive rate increased from 87.9% to 92.9% using 1/49 model parameters. His models can be combined with deep learning based generative material design models from Dr. Hu’s group for large-scale screening of novel functional materials. The accepted manuscript “Materials synthesizability and stability prediction using Semi-supervised teacher-student dual neural network” can be downloaded from here. Daniel was a recipient of USC Magellan Scholarship. Dr. Hu’s machine learning and evolution laboratory (MLEG) has involved dozens of undergraduate students in their cutting-edge research on AI for science and deep learning for materials discovery, which has already led to four journal publications in leading materials science journals. Interested highly motivated students can contact Dr. Hu by email.

New materials discovered in this research