Deep Learning-Based Classification of Gamma Photon Interactions in Room-Temperature Semiconductor Radiation Detectors

no code implementations • 1 Nov 2023 • Sandeep K. Chaudhuri, Qinyang Li, Krishna C. Mandal, Jianjun Hu

Photon counting radiation detectors have become an integral part of medical imaging modalities such as Positron Emission Tomography or Computed Tomography.

Materials Transformers Language Models for Generative Materials Design: a benchmark study

1 code implementation • 27 Jun 2022 • Nihang Fu, Lai Wei, Yuqi Song, Qinyang Li, Rui Xin, Sadman Sadeed Omee, Rongzhi Dong, Edirisuriya M. Dilanga Siriwardane, Jianjun Hu

We also find that the properties of the generated samples can be tailored by training the models with selected training sets such as high-bandgap materials.

Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials

no code implementations • 25 Apr 2022 • Lai Wei, Qinyang Li, Yuqi Song, Stanislav Stefanov, Edirisuriya M. D. Siriwardane, Fanglin Chen, Jianjun Hu

Here we propose BLMM Crystal Transformer, a neural network based probabilistic generative model for generative and tinkering design of inorganic materials.

Language Modelling Self-Learning +1

Scalable deeper graph neural networks for high-performance materials property prediction

1 code implementation • 25 Sep 2021 • Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu, Lai Wei, Sourin Dey, Rongzhi Dong, Qinyang Li, Jianjun Hu

Machine learning (ML) based materials discovery has emerged as one of the most promising approaches for breakthroughs in materials science.

Band Gap Graph Attention +3