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Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning
Engineering and Technologynpj Computational Materials

Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning

S. Pathrudkar, P. Thiagarajan, et al.

This research, conducted by Shashank Pathrudkar, Ponkrshnan Thiagarajan, Shivang Agarwal, Amartya S. Banerjee, and Susanta Ghosh, tackles the challenges of Kohn-Sham Density Functional Theory simulations by employing transfer learning and Bayesian neural networks. This innovative approach allows for confident predictions in material properties at multi-million-atom scales with limited computational resources.... show more
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