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Deep learning density functional theory Hamiltonian in real space

Autor: Yuan, Zilong, Tang, Zechen, Tao, Honggeng, Gong, Xiaoxun, Chen, Zezhou, Wang, Yuxiang, Li, He, Li, Yang, Xu, Zhiming, Sun, Minghui, Zhao, Boheng, Wang, Chong, Duan, Wenhui, Xu, Yong

Deep learning electronic structures from ab initio calculations holds great potential to revolutionize computational materials studies. While existing methods proved success in deep-learning density functional theory (DFT) Hamiltonian matrices, they

Externí odkaz: http://arxiv.org/abs/2407.14379

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Improving density matrix electronic structure method by deep learning

Autor: Tang, Zechen, Zou, Nianlong, Li, He, Wang, Yuxiang, Yuan, Zilong, Tao, Honggeng, Li, Yang, Chen, Zezhou, Zhao, Boheng, Sun, Minghui, Jiang, Hong, Duan, Wenhui, Xu, Yong

The combination of deep learning and ab initio materials calculations is emerging as a trending frontier of materials science research, with deep-learning density functional theory (DFT) electronic structure being particularly promising. In this work

Externí odkaz: http://arxiv.org/abs/2406.17561

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Universal materials model of deep-learning density functional theory Hamiltonian

Autor: Wang, Yuxiang, Li, Yang, Tang, Zechen, Li, He, Yuan, Zilong, Tao, Honggeng, Zou, Nianlong, Bao, Ting, Liang, Xinghao, Chen, Zezhou, Xu, Shanghua, Bian, Ce, Xu, Zhiming, Wang, Chong, Si, Chen, Duan, Wenhui, Xu, Yong

Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challenging objective remains elusive. Here, we propose a feasible pathway to

Externí odkaz: http://arxiv.org/abs/2406.10536

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Neural-network Density Functional Theory Based on Variational Energy Minimization

Autor: Li, Yang, Tang, Zechen, Chen, Zezhou, Sun, Minghui, Zhao, Boheng, Li, He, Tao, Honggeng, Yuan, Zilong, Duan, Wenhui, Xu, Yong

Publikováno v: Phys. Rev. Lett. 133, 076401 (2024)

Deep-learning density functional theory (DFT) shows great promise to significantly accelerate material discovery and potentially revolutionize materials research. However, current research in this field primarily relies on data-driven supervised lear

Externí odkaz: http://arxiv.org/abs/2403.11287

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Equivariant Neural Network Force Fields for Magnetic Materials

Autor: Yuan, Zilong, Xu, Zhiming, Li, He, Cheng, Xinle, Tao, Honggeng, Tang, Zechen, Zhou, Zhiyuan, Duan, Wenhui, Xu, Yong

Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is still in its early stage due to challenges posed by the subtle magnetic en

Externí odkaz: http://arxiv.org/abs/2402.04864

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DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer

Autor: Wang, Yuxiang, Li, He, Tang, Zechen, Tao, Honggeng, Wang, Yanzhen, Yuan, Zilong, Chen, Zezhou, Duan, Wenhui, Xu, Yong

Deep-learning electronic structure calculations show great potential for revolutionizing the landscape of computational materials research. However, current neural-network architectures are not deemed suitable for widespread general-purpose applicati

Externí odkaz: http://arxiv.org/abs/2401.17015

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