Origins of structural and electronic transitions in disordered silicon
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DOI: 10.1038/s41586-020-03072-z
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- Bo Lin & Jian Jiang & Xiao Cheng Zeng & Lei Li, 2023. "Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
- Zhao Fan & Hajime Tanaka, 2024. "Microscopic mechanisms of pressure-induced amorphous-amorphous transitions and crystallisation in silicon," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
- Huziel E. Sauceda & Luis E. Gálvez-González & Stefan Chmiela & Lauro Oliver Paz-Borbón & Klaus-Robert Müller & Alexandre Tkatchenko, 2022. "BIGDML—Towards accurate quantum machine learning force fields for materials," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
- Adil Kabylda & Valentin Vassilev-Galindo & Stefan Chmiela & Igor Poltavsky & Alexandre Tkatchenko, 2023. "Efficient interatomic descriptors for accurate machine learning force fields of extended molecules," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
- Ang Gao & Richard C. Remsing, 2022. "Self-consistent determination of long-range electrostatics in neural network potentials," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- Daniel Hedman & Ben McLean & Christophe Bichara & Shigeo Maruyama & J. Andreas Larsson & Feng Ding, 2024. "Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
- Wenzhu Liu & Jianhua Shi & Liping Zhang & Anjun Han & Shenglei Huang & Xiaodong Li & Jun Peng & Yuhao Yang & Yajun Gao & Jian Yu & Kai Jiang & Xinbo Yang & Zhenfei Li & Wenjie Zhao & Junlin Du & Xin S, 2022. "Light-induced activation of boron doping in hydrogenated amorphous silicon for over 25% efficiency silicon solar cells," Nature Energy, Nature, vol. 7(5), pages 427-437, May.
- Hanwen Zhang & Veronika Juraskova & Fernanda Duarte, 2024. "Modelling chemical processes in explicit solvents with machine learning potentials," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
- Jonathan Vandermause & Yu Xie & Jin Soo Lim & Cameron J. Owen & Boris Kozinsky, 2022. "Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
- Di Zhang & Peiyun Yi & Xinmin Lai & Linfa Peng & Hao Li, 2024. "Active machine learning model for the dynamic simulation and growth mechanisms of carbon on metal surface," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
- Linus C. Erhard & Jochen Rohrer & Karsten Albe & Volker L. Deringer, 2024. "Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
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