Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms
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DOI: 10.1038/s41467-024-50531-6
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- Andrew W. Senior & Richard Evans & John Jumper & James Kirkpatrick & Laurent Sifre & Tim Green & Chongli Qin & Augustin Žídek & Alexander W. R. Nelson & Alex Bridgland & Hugo Penedones & Stig Petersen, 2020. "Improved protein structure prediction using potentials from deep learning," Nature, Nature, vol. 577(7792), pages 706-710, January.
- Giacomo E. M. Crisenza & Paolo Melchiorre, 2020. "Chemistry glows green with photoredox catalysis," Nature Communications, Nature, vol. 11(1), pages 1-4, December.
- David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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